Mike West | Scholars@Duke

Mike West
Arts and Sciences Distinguished Professor of Statistics and Decision Sciences

Go to my personal web page for links and info on my teaching, publication list (sortable and searchable -- just click on table headers), current research, current & past students, software, etc.

Current Appointments & Affiliations

Arts and Sciences Distinguished Professor of Statistics and Decision Sciences, Statistical Science, Trinity College of Arts & Sciences 1999
Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 1992
Member of the Duke Cancer Institute, Duke Cancer Institute, Institutes and Centers 1988

Contact Information

214C Old Chemistry, Durham, NC 27708
Box 90251, Durham, NC 27708-0251
(919) 684-8842

Background
Duke Appointment History
- Director of Graduate Studies in the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2010 - 2014
- Director, Institute of Statistics and Decision Sciences, Statistical Science, Trinity College of Arts & Sciences 1990 - 2002
- Associate Professor, Statistical Science, Trinity College of Arts & Sciences 1988 - 1992
Recognition
In the News
- NOV 7, 2018
  
  Statistical Science Professor Awarded for Contributions to Bayesian Analysis
- OCT 13, 2014
  
  Mike West Honored by NC Chapter of American Statistical Association
- SEP 25, 2014
  
  West Honored With Inaugural Award of International Bayesian Society
- JUN 25, 2014
  
  Ruilin Zhong: Going Far With Interests in Economics and Statistics
Awards & Honors
Publications & Artistic Works
Selected Publications
- Books
  - West, Mike, and Jeff Harrison. Bayesian Forecasting and Dynamic Models. Springer Science & Business Media, 2013.
  - O’ Hagan, Anthony, and Mike West, editors. The Oxford Handbook of Applied Bayesian Analysis. 2010.
  - Prado, R., and M. West. Time series: Modeling, computation, and inference. 2010, pp. 1–354. Scopus, doi:10.1201/9781439882757.
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  - Pole, A., et al. Applied Bayesian Forecasting & Time Series Analysis. Chapman-Hall, 1994.
- Academic Articles
  - McAlinn, K., and M. West. “Dynamic Bayesian predictive synthesis in time series forecasting.” Journal of Econometrics, vol. 210, no. 1, May 2019, pp. 155–69. Scopus, doi:10.1016/j.jeconom.2018.11.010.
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  - Berry, L. R., and M. West. “Bayesian Forecasting of Many Count-Valued Time Series.” Journal of Business and Economic Statistics, Jan. 2019. Scopus, doi:10.1080/07350015.2019.1604372.
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  - Berry, L. R., et al. “Probabilistic forecasting of heterogeneous consumer transaction–sales time series.” International Journal of Forecasting, Jan. 2019. Scopus, doi:10.1016/j.ijforecast.2019.07.007.
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  - Chen, X., et al. “Bayesian dynamic modeling and monitoring of network flows.” Network Science, Jan. 2019. Scopus, doi:10.1017/nws.2019.10.
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  - Crawford, L., et al. “Variable prioritization in nonlinear black box methods: A genetic association case study.” Annals of Applied Statistics, vol. 13, no. 2, Jan. 2019, pp. 958–89. Scopus, doi:10.1214/18-AOAS1222.
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  - Irie, K., and M. West. “Bayesian emulation for multi-step optimization in decision problems.” Bayesian Analysis, vol. 14, no. 1, Jan. 2019, pp. 137–60. Scopus, doi:10.1214/18-BA1105.
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  - McAlinn, K., et al. “Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting.” Journal of the American Statistical Association, Jan. 2019. Scopus, doi:10.1080/01621459.2019.1660171.
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  - West, M. “Reply to Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions”.” Annals of the Institute of Statistical Mathematics, Jan. 2019. Scopus, doi:10.1007/s10463-019-00744-0.
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  - Yao, Yuling, et al. “Using Stacking to Average Bayesian Predictive Distributions (with Discussion).” Bayesian Analysis, vol. 13, no. 3, Institute of Mathematical Statistics, Sept. 2018, pp. 917–1007. Crossref, doi:10.1214/17-ba1091.
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  - Chen, X., et al. “Scalable Bayesian Modeling, Monitoring, and Analysis of Dynamic Network Flow Data.” Journal of the American Statistical Association, vol. 113, no. 522, Apr. 2018, pp. 519–33. Scopus, doi:10.1080/01621459.2017.1345742.
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  - Gruber, L. F., and M. West. “Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models.” Econometrics and Statistics, vol. 3, July 2017, pp. 3–22. Scopus, doi:10.1016/j.ecosta.2017.03.003.
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  - Nakajima, J., and M. West. “Dynamics & sparsity in latent threshold factor models: A study in multivariate EEG signal processing.” Brazilian Journal of Probability and Statistics, vol. 31, no. 4, Jan. 2017, pp. 701–31. Scopus, doi:10.1214/17-BJPS364.
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  - Zhao, Z. Y., et al. “Dynamic dependence networks: Financial time series forecasting and portfolio decisions.” Applied Stochastic Models in Business and Industry, vol. 32, no. 3, May 2016, pp. 311–32. Scopus, doi:10.1002/asmb.2161.
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  - Zhao, Z., et al. “Rejoinder to ‘Dynamic dependence networks: Financial time series forecasting and portfolio decisions’.” Applied Stochastic Models in Business and Industry, vol. 32, no. 3, May 2016, pp. 336–39. Scopus, doi:10.1002/asmb.2169.
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  - Lin, Lin, et al. “Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies..” Biostatistics (Oxford, England), vol. 17, no. 1, Jan. 2016, pp. 40–53. Epmc, doi:10.1093/biostatistics/kxv021.
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  - Nakajima, J., and M. West. “Dynamic network signal processing using latent threshold models.” Digital Signal Processing: A Review Journal, vol. 47, Dec. 2015, pp. 5–16. Scopus, doi:10.1016/j.dsp.2015.04.008.
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  - Kontoghiorghes, E. J., et al. “CFEnetwork: The Annals of computational and financial econometrics: 2nd issue.” Computational Statistics and Data Analysis, vol. 76, Jan. 2014, pp. 1–3. Scopus, doi:10.1016/j.csda.2014.04.006.
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  - Kontoghiorghes, Erricos John, et al. “CFEnetwork: The Annals of Computational and Financial Econometrics: 2nd Issue..” Computational Statistics & Data Analysis, vol. 76, 2014, pp. 1–3.
  - Mukherjee, C., et al. “Spatially varying SAR models and Bayesian inference for high-resolution lattice data.” Annals of the Institute of Statistical Mathematics, vol. 66, no. 3, Jan. 2014, pp. 473–94. Scopus, doi:10.1007/s10463-013-0426-9.
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  - Zhou, X., et al. “Bayesian forecasting and portfolio decisions using dynamic dependent factor models.” International Journal of Forecasting, vol., 2014, p.
  - Zhou, X., et al. “Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models.” International Journal of Forecasting, vol. 30, no. 4, Jan. 2014, pp. 963–80. Scopus, doi:10.1016/j.ijforecast.2014.03.017.
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  - Lin, Lin, et al. “Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies..” Stat Appl Genet Mol Biol, vol. 12, no. 3, June 2013, pp. 309–31. Pubmed, doi:10.1515/sagmb-2012-0001.
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  - Nakajima, J., and M. West. “Bayesian Analysis of Latent Threshold Dynamic Models.” Journal of Business and Economic Statistics, vol. 31, no. 2, Apr. 2013, pp. 151–64. Scopus, doi:10.1080/07350015.2012.747847.
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  - Cron, Andrew, et al. “Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples..” Plos Comput Biol, vol. 9, no. 7, 2013. Pubmed, doi:10.1371/journal.pcbi.1003130.
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  - Manolopoulou, I., et al. “Bayesian spatio-dynamic modeling in cell motility studies: Learning nonlinear taxic fields guiding the immune response.” Journal of the American Statistical Association, vol. 107, no. 499, Dec. 2012, pp. 855–65. Scopus, doi:10.1080/01621459.2012.655995.
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  - Manolopoulou, I., et al. “Rejoinder.” Journal of the American Statistical Association, vol. 107, no. 499, Dec. 2012, pp. 871–74. Scopus, doi:10.1080/01621459.2012.714715.
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  - Nakajima, J., and M. West. “Dynamic factor volatility modeling: A bayesian latent threshold approach.” Journal of Financial Econometrics, vol. 11, no. 1, Dec. 2012, pp. 116–53. Scopus, doi:10.1093/jjfinec/nbs013.
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  - Wang, Quanli, and Mike West. “Model-controlled flooding with applications to image reconstruction and segmentation..” Journal of Electronic Imaging, vol. 21, no. 2, June 2012. Epmc, doi:10.1117/1.JEI.21.2.023020.
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  - Bonassi, Fernando V., et al. “Bayesian learning from marginal data in bionetwork models..” Statistical Applications in Genetics and Molecular Biology, vol. 10, no. 1, Oct. 2011. Epmc, doi:10.2202/1544-6115.1684.
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  - Hallen, Mark, et al. “Computation of steady-state probability distributions in stochastic models of cellular networks..” Plos Computational Biology, vol. 7, no. 10, Oct. 2011. Epmc, doi:10.1371/journal.pcbi.1002209.
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  - Mukherjee, C., et al. “Bayesian statistical modeling of spatially correlated error structure in atmospheric tracer inverse analysis.” Atmospheric Chemistry and Physics, vol. 11, no. 11, July 2011, pp. 5365–82. Scopus, doi:10.5194/acp-11-5365-2011.
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  - Yao, Guang, et al. “Origin of bistability underlying mammalian cell cycle entry..” Molecular Systems Biology, vol. 7, Apr. 2011. Epmc, doi:10.1038/msb.2011.19.
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  - Cron, Andrew J., and Mike West. “Efficient Classification-Based Relabeling in Mixture Models..” The American Statistician, vol. 65, no. 1, Feb. 2011, pp. 16–20. Epmc, doi:10.1198/tast.2011.10170.
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  - Chan, Cliburn, et al. “Optimization of a highly standardized carboxyfluorescein succinimidyl ester flow cytometry panel and gating strategy design using discriminative information measure evaluation..” Cytometry A, vol. 77, no. 12, Dec. 2010, pp. 1126–36. Pubmed, doi:10.1002/cyto.a.20987.
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  - Manolopoulou, I., et al. “Rejoinder.” Bayesian Analysis, vol. 5, no. 3, Dec. 2010, pp. 461–64. Scopus, doi:10.1214/10-BA517REJ.
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  - Chen, Julia Ling-Yu, et al. “Lactic acidosis triggers starvation response with paradoxical induction of TXNIP through MondoA..” Plos Genet, vol. 6, no. 9, Sept. 2010. Pubmed, doi:10.1371/journal.pgen.1001093.
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  - Niemi, Jarad, and Mike West. “Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models..” Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 19, no. 2, June 2010, pp. 260–80. Epmc, doi:10.1198/jcgs.2010.08117.
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  - Suchard, Marc A., et al. “Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures..” J Comput Graph Stat, vol. 19, no. 2, June 2010, pp. 419–38. Pubmed, doi:10.1198/jcgs.2010.10016.
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  - Yoshida, Ryo, and Mike West. “Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing..” Journal of Machine Learning Research : Jmlr, vol. 11, May 2010, pp. 1771–98.
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  - Lin, L., et al. “Discriminative information analysis in mixture modelling.” Working Paper, Department of Statistical Science, Duke University, no. 10–23, 2010.
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  - Manolopoulou, I., et al. “Selection sampling from large data sets for targeted inference in mixture modeling.” Bayesian Analysis, vol. 5, no. 3, 2010, pp. 429–50. Scival, doi:10.1214/10-BA517.
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  - Manolopoulou, Ioanna, et al. “Selection Sampling from Large Data Sets for Targeted Inference in Mixture Modeling..” Bayesian Analysis, vol. 5, no. 3, Jan. 2010, pp. 1–22.
  - Suchard, M. A., et al. “Some of the What?, Why?, How?, Who? and Where? of graphics processing unit computing for Bayesian analysis.” Bulletin of the International Society for Bayesian Analysis, vol. 17, 2010, pp. 12–16.
  - Wang, Quanli, et al. “Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy..” Cytometry. Part a : The Journal of the International Society for Analytical Cytology, vol. 77, no. 1, Jan. 2010, pp. 101–10. Epmc, doi:10.1002/cyto.a.20812.
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  - Yoshida, R., and M. West. “Bayesian learning in sparse graphical factor models via annealed entropy.” Journal of Machine Learning Research, vol. 11, 2010, pp. 1771–98.
  - Ji, Chunlin, et al. “Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology..” Bayesian Analysis, vol. 4, no. 2, Dec. 2009, pp. 297–316. Epmc, doi:10.1214/09-ba411.
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  - Liu, F., and M. West. “A dynamic modelling strategy for bayesian computer model emulation.” Bayesian Analysis, vol. 4, no. 2, Dec. 2009, pp. 393–412. Scopus, doi:10.1214/09-BA415.
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  - Wang, Hao, and Mike West. “Bayesian analysis of matrix normal graphical models..” Biometrika, vol. 96, no. 4, Dec. 2009, pp. 821–34. Epmc, doi:10.1093/biomet/asp049.
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  - Chang, Jeffrey T., et al. “A genomic strategy to elucidate modules of oncogenic pathway signaling networks..” Molecular Cell, vol. 34, no. 1, Apr. 2009, pp. 104–14. Epmc, doi:10.1016/j.molcel.2009.02.030.
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  - Lucas, J. E., et al. “Cross-study projections of genomic biomarkers: An evaluation in cancer genomics.” Plos One, vol. 4, no. 2, Feb. 2009. Scopus, doi:10.1371/journal.pone.0004523.
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  - Lucas, Joseph E., et al. “Cross-study projections of genomic biomarkers: an evaluation in cancer genomics..” Plos One, vol. 4, no. 2, 2009. Pubmed, doi:10.1371/journal.pone.0004523.
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  - Lucas, Joseph, et al. “A bayesian analysis strategy for cross-study translation of gene expression biomarkers..” Statistical Applications in Genetics and Molecular Biology, vol. 8, Jan. 2009, p. Article-11. Epmc, doi:10.2202/1544-6115.1436.
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  - Merl, Daniel, et al. “AN INTEGRATIVE ANALYSIS OF CANCER GENE EXPRESSION STUDIES USING BAYESIAN LATENT FACTOR MODELING..” Ann Appl Stat, vol. 3, no. 4, 2009, pp. 1675–94. Pubmed, doi:10.1214/09-aoas261.
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  - Mukherjee, C., and M. West. “Sequential Monte Carlo in model comparison: Example in cellular dynamics in systems biology.” Jsm Proceedings,Section on Bayesian Statistical Science. Alexandria, Va: American Statistical Association, 2009, pp. 1274–87.
  - Carvalho, Carlos M., et al. “High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics..” Journal of the American Statistical Association, vol. 103, no. 484, Dec. 2008, pp. 1438–56. Epmc, doi:10.1198/016214508000000869.
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  - Chen, Julia Ling-Yu, et al. “The genomic analysis of lactic acidosis and acidosis response in human cancers..” Plos Genet, vol. 4, no. 12, Dec. 2008. Pubmed, doi:10.1371/journal.pgen.1000293.
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  - Chan, Cliburn, et al. “Statistical mixture modeling for cell subtype identification in flow cytometry..” Cytometry A, vol. 73, no. 8, Aug. 2008, pp. 693–701. Pubmed, doi:10.1002/cyto.a.20583.
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  - Goldschmidt-Clermont, Pascal J., et al. “Of cardiovascular illness and diversity of biological response..” Trends in Cardiovascular Medicine, vol. 18, no. 5, July 2008, pp. 194–97. Epmc, doi:10.1016/j.tcm.2008.07.003.
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  - Clarke, J., and M. West. “Bayesian Weibull tree models for survival analysis of clinico-genomic data.” Statistical Methodology, vol. 5, no. 3, May 2008, pp. 238–62. Scopus, doi:10.1016/j.stamet.2007.09.003.
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  - Chen, J. L., et al. “Genomic analysis of response to lactic acidosis and acidosis in human cancers.” Plos Genetics, vol. 4, no. 12, 2008.
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  - Xu, M., et al. “Characterizing disease-specific pathways and their coordination by integrative microarray analysis with application to cancer.” Bmc Genomics, vol. 9, 2008, p. Supp1:S12-Supp1:S12.
  - Xu, Min, et al. “An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer..” Bmc Genomics, vol. 9 Suppl 1, Jan. 2008. Epmc, doi:10.1186/1471-2164-9-S1-S12.
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  - Carvalho, C. M., and M. West. “Dynamic matrix-variate graphical models.” Bayesian Analysis, vol. 2, no. 1, Dec. 2007, pp. 69–98. Scopus, doi:10.1214/07-BA204.
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  - Liang, F., et al. “Understanding the use of unlabelled data in predictive modelling.” Statistical Science, vol. 22, no. 2, Oct. 2007, pp. 189–205.
  - Carvalho, C. M., et al. “Simulation of hyper-inverse Wishart distributions in graphical models.” Biometrika, vol. 94, no. 3, Sept. 2007, pp. 647–59. Scopus, doi:10.1093/biomet/asm056.
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  - Hans, C., et al. “Shotgun stochastic search for "large p" regression.” Journal of the American Statistical Association, vol. 102, no. 478, June 2007, pp. 507–16. Scopus, doi:10.1198/016214507000000121.
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  - Seo, David M., et al. “Of mice and men: Sparse statistical modeling in cardiovascular genomics.” The Annals of Applied Statistics, vol. 1, no. 1, Institute of Mathematical Statistics, June 2007, pp. 152–78. Crossref, doi:10.1214/07-aoas110.
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  - Liang, F., et al. “The use of unlabeled data in predictive modeling.” Statistical Science, vol. 22, no. 2, May 2007, pp. 189–205. Scopus, doi:10.1214/088342307000000032.
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  - Wu, Y., et al. “Bayesian CART: Prior specification and posterior simulation.” Journal of Computational and Graphical Statistics, vol. 16, no. 1, Mar. 2007, pp. 44–66. Scopus, doi:10.1198/106186007X180426.
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  - Carvalho, C. M., and M. West. “Dynamic matrix-variate graphical models.” Bayesian Analysis, vol. 2, 2007, pp. 69–98.
  - Hans, C., et al. “Shotgun stochastic search in regression with many predictors.” Journal of the American Statistical Association, vol. 102, 2007, pp. 507–16.
  - Hans, C., et al. “SSS: High-dimensional Bayesian regression model search.” Bulletin of the International Society for Bayesian Analysis, vol. 24, 2007, pp. 8–9.
  - Liang, F., et al. “Non-parametric Bayesian kernel models.” Department of Statistical Science, Duke University, 2007.
  - Wang, Q., et al. “BFRM: Bayesian factor regression modelling.” Bulletin of the International Society for Bayesian Analysis, vol. 14, 2007, pp. 4–5.
  - Wu, Y., et al. “Bayesian CART: Prior structure and MCMC computations.” Journal of Computational and Graphical Statistics, vol. 16, 2007, pp. 44–66.
  - Ferreira, M. A. R., et al. “Multi-scale and hidden resolution time series models.” Bayesian Analysis, vol. 1, no. 4, Dec. 2006, pp. 947–68. Scopus, doi:10.1214/06-BA131.
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  - Cheng, Skye H., et al. “Genomic prediction of locoregional recurrence after mastectomy in breast cancer..” J Clin Oncol, vol. 24, no. 28, Oct. 2006, pp. 4594–602. Pubmed, doi:10.1200/JCO.2005.02.5676.
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  - West, Mike, et al. “Embracing the complexity of genomic data for personalized medicine..” Genome Res, vol. 16, no. 5, May 2006, pp. 559–66. Pubmed, doi:10.1101/gr.3851306.
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  - Cheng, Skye Hongiun, et al. “Prognostic index score and clinical prediction model of local regional recurrence after mastectomy in breast cancer patients..” Int J Radiat Oncol Biol Phys, vol. 64, no. 5, Apr. 2006, pp. 1401–09. Pubmed, doi:10.1016/j.ijrobp.2005.11.015.
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  - Dobra, A., et al. “Data augmentation in multi-way contingency tables with fixed marginal totals.” Journal of Statistical Planning and Inference, vol. 136, no. 2, Feb. 2006, pp. 355–72. Scopus, doi:10.1016/j.jspi.2004.07.002.
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  - Dressman, Holly K., et al. “Gene expression profiles of multiple breast cancer phenotypes and response to neoadjuvant chemotherapy..” Clin Cancer Res, vol. 12, no. 3 Pt 1, Feb. 2006, pp. 819–26. Pubmed, doi:10.1158/1078-0432.CCR-05-1447.
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  - Bild, Andrea H., et al. “Oncogenic pathway signatures in human cancers as a guide to targeted therapies..” Nature, vol. 439, no. 7074, Jan. 2006, pp. 353–57. Pubmed, doi:10.1038/nature04296.
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  - Ferreira, M. A. R., et al. “Multiscale and hidden resolution time series models.” Bayesian Analysis, vol. 2, 2006, pp. 294–314.
  - Hans, C., and M. West. “High-dimensional regression in cancer genomics.” Bulletin of the International Society for Bayesian Analysis, vol. 13, 2006, pp. 2–3.
  - Karra, R., et al. “Stem Cells of Aging Donors- Insufficient Capacity to Repair Causes & Progression of Atherosclerosis in the Recipient.” Journal of the American Society of Nephrology, vol. 17, 2006, pp. 317–22.
  - Jones, B., and M. West. “Covariance decomposition in undirected Gaussian graphical models.” Biometrika, vol. 92, no. 4, Dec. 2005, pp. 779–86. Scopus, doi:10.1093/biomet/92.4.779.
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  - Karra, Ravi, et al. “Molecular evidence for arterial repair in atherosclerosis..” Proc Natl Acad Sci U S A, vol. 102, no. 46, Nov. 2005, pp. 16789–94. Pubmed, doi:10.1073/pnas.0507718102.
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  - Black, Esther P., et al. “Distinctions in the specificity of E2F function revealed by gene expression signatures..” Proc Natl Acad Sci U S A, vol. 102, no. 44, Nov. 2005, pp. 15948–53. Pubmed, doi:10.1073/pnas.0504300102.
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  - Jones, B., et al. “Experiments in stochastic computation for high-dimensional graphical models.” Statistical Science, vol. 20, no. 4, Nov. 2005, pp. 388–400. Scopus, doi:10.1214/088342305000000304.
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  - Delong, Mark, et al. “DIG--a system for gene annotation and functional discovery..” Bioinformatics, vol. 21, no. 13, July 2005, pp. 2957–59. Pubmed, doi:10.1093/bioinformatics/bti467.
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  - Petersen, R. P., et al. “Gene expression signatures for prognosis in NSCLC, coupled with signatures of oncogenic pathway deregulation, provide a novel approach for selection of molecular targets..” J Clin Oncol, vol. 23, no. 16_suppl, June 2005.
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  - Berchuck, Andrew, et al. “Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers..” Clin Cancer Res, vol. 11, no. 10, May 2005, pp. 3686–96. Pubmed, doi:10.1158/1078-0432.CCR-04-2398.
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  - Rich, Jeremy N., et al. “Gene expression profiling and genetic markers in glioblastoma survival..” Cancer Res, vol. 65, no. 10, May 2005, pp. 4051–58. Pubmed, doi:10.1158/0008-5472.CAN-04-3936.
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  - Pittman, Jennifer, et al. “Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes..” Biostatistics, vol. 5, no. 4, Oct. 2004, pp. 587–601. Pubmed, doi:10.1093/biostatistics/kxh011.
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  - Seo, David, et al. “Gene expression phenotypes of atherosclerosis..” Arterioscler Thromb Vasc Biol, vol. 24, no. 10, Oct. 2004, pp. 1922–27. Pubmed, doi:10.1161/01.ATV.0000141358.65242.1f.
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  - Huang, Erich S., et al. “An overview of genomic data analysis..” Surgery, vol. 136, no. 3, Sept. 2004, pp. 497–99. Pubmed, doi:10.1016/j.surg.2004.05.037.
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  - Pittman, Jennifer, et al. “Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes..” Proc Natl Acad Sci U S A, vol. 101, no. 22, June 2004, pp. 8431–36. Pubmed, doi:10.1073/pnas.0401736101.
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  - Berchuck, Andrew, et al. “Prediction of optimal versus suboptimal cytoreduction of advanced-stage serous ovarian cancer with the use of microarrays..” Am J Obstet Gynecol, vol. 190, no. 4, Apr. 2004, pp. 910–25. Pubmed, doi:10.1016/j.ajog.2004.02.005.
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  - Godsill, S. J., et al. “Monte carlo smoothing for nonlinear time series.” Journal of the American Statistical Association, vol. 99, no. 465, Mar. 2004, pp. 156–68. Scopus, doi:10.1198/016214504000000151.
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  - Dobra, A., et al. “Sparse graphical models for exploring gene expression data.” Journal of Multivariate Analysis, vol. 90, no. 1 SPEC. ISS., Jan. 2004, pp. 196–212. Scopus, doi:10.1016/j.jmva.2004.02.009.
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  - Hoffman, E. P., et al. “Expression profiling: Best practices for Affymetrix array data generation and interpretation for clinical trials.” Nature Reviews Genetics, vol. 5, 2004, pp. 229–37.
  - Lopes, H. F., and M. West. “Bayesian model assessment in factor analysis.” Statistica Sinica, vol. 14, no. 1, Jan. 2004, pp. 41–67.
  - Nevins, Joseph R., et al. “Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction..” Hum Mol Genet, vol. 12 Spec No 2, Oct. 2003, pp. R153–57. Pubmed, doi:10.1093/hmg/ddg287.
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  - Huang, Erich S., et al. “Gene expression phenotypes of oncogenic signaling pathways..” Cell Cycle, vol. 2, no. 5, Sept. 2003, pp. 415–17.
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  - Huang, E., et al. “Erratum: Gene expression phenotypic models that predict the activity of oncogenic pathways (Nature Genetics (2003) 34 (226-230)).” Nature Genetics, vol. 34, no. 4, Aug. 2003. Scopus, doi:10.1038/ng0803-465.
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  - Black, Esther P., et al. “Distinct gene expression phenotypes of cells lacking Rb and Rb family members..” Cancer Res, vol. 63, no. 13, July 2003, pp. 3716–23.
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  - Huang, Erich, et al. “Gene expression phenotypic models that predict the activity of oncogenic pathways..” Nat Genet, vol. 34, no. 2, June 2003, pp. 226–30. Pubmed, doi:10.1038/ng1167.
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  - Huang, Erich, et al. “Gene expression predictors of breast cancer outcomes..” Lancet, vol. 361, no. 9369, May 2003, pp. 1590–96. Pubmed, doi:10.1016/S0140-6736(03)13308-9.
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  - Paddock, S. M., et al. “Randomized Polya tree models for nonparametric Bayesian inference.” Statistica Sinica, vol. 13, no. 2, Apr. 2003, pp. 443–60.
  - Huang, Erich, et al. “Gene expression profiling for prediction of clinical characteristics of breast cancer..” Recent Prog Horm Res, vol. 58, 2003, pp. 55–73. Pubmed, doi:10.1210/rp.58.1.55.
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  - Paddock, S., et al. “Tree models for nonparametric Bayesian inference.” Statistica Sinica, vol. 13, 2003, pp. 443–60.
  - Pittman, J., et al. “Prediction tree models in clinico-genomics.” Bulletin of the International Statistical Institute, vol. 54, 2003, pp. 76–76.
  - Fong, W., et al. “Monte Carlo smoothing with application to audio signal enhancement.” Ieee Transactions on Signal Processing, vol. 50, no. 2, Feb. 2002, pp. 438–49. Scopus, doi:10.1109/78.978397.
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  - Lee, H. K. H., et al. “Markov random field models for high-dimensional parameters in simulations of fluid flow in porous media.” Technometrics, vol. 44, no. 3, Jan. 2002, pp. 230–41. Scopus, doi:10.1198/004017002188618419.
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  - Spang, Rainer, et al. “Prediction and uncertainty in the analysis of gene expression profiles..” In Silico Biol, vol. 2, no. 3, 2002, pp. 369–81.
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  - Tebaldi, C., et al. “Statistical analyses of freeway traffic flows.” Journal of Forecasting, vol. 21, no. 1, Jan. 2002, pp. 39–68. Scopus, doi:10.1002/for.804.
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  - West, M., et al. “Predicting the clinical status of human breast cancer by using gene expression profiles..” Proc Natl Acad Sci U S A, vol. 98, no. 20, Sept. 2001, pp. 11462–67. Pubmed, doi:10.1073/pnas.201162998.
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  - Ishida, S., et al. “Role for E2F in control of both DNA replication and mitotic functions as revealed from DNA microarray analysis..” Mol Cell Biol, vol. 21, no. 14, July 2001, pp. 4684–99. Pubmed, doi:10.1128/MCB.21.14.4684-4699.2001.
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  - Godsill, S, Doucet, A, and West, M. "Maximum a posteriori sequence estimation using Monte Carlo particle filters." ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 53.1 (March 2001): 82-96. Full Text Link to Item
  - Prado, R., et al. “Multi-channel EEG analyses via dynamic regression models with time-varying lag/lead structure.” Journal of the Royal Statistical Society (Ser. C), vol. 50, 2001, pp. 95–110.
  - Prado, R., et al. “Bayesian time-varying autoregressions: Theory, methods and applications.” Resenhas, vol. 4, 2001, pp. 405–22.
  - Prado, R., et al. “Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag-lead structure.” Journal of the Royal Statistical Society. Series C: Applied Statistics, vol. 50, no. 1, Jan. 2001, pp. 95–109. Scopus, doi:10.1111/1467-9876.00222.
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  - Spang, R., et al. “Prediction and uncertainty in the analysis of gene expression profiles.” In Silico Biology, vol. 2, 2001, pp. 0033–0033.
  - Huerta, G., and M. West. “Priors and component structures in autoregressive time series models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 62, no. 2, Wiley, May 2000, pp. 429–429. Crossref, doi:10.1111/1467-9868.00241.
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  - Aguilar, O., and M. West. “Bayesian dynamic factor models and portfolio allocation.” Journal of Business and Economic Statistics, vol. 18, no. 3, Jan. 2000, pp. 338–57. Scopus, doi:10.1080/07350015.2000.10524875.
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  - Burgess, J., et al. “Profiling mental health provider trends in health care delivery systems.” Health Services & Outcomes Research Methodology, vol. 100, 2000, pp. 253–76.
  - Doucet, A., et al. “Monte Carlo filtering and smoothing with application to time-varying spectral estimation.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, vol. 2, Jan. 2000, pp. 701–04. Scopus, doi:10.1109/ICASSP.2000.859056.
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  - Krystal, A. D., et al. “EEG effects of ECT: implications for rTMS..” Depress Anxiety, vol. 12, no. 3, 2000, pp. 157–65. Pubmed, doi:10.1002/1520-6394(2000)12:3<157::AID-DA7>3.0.CO;2-R.
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  - Krystal, A. D., et al. “New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions..” Clin Neurophysiol, vol. 110, no. 12, Dec. 1999, pp. 2197–206. Pubmed, doi:10.1016/s1388-2457(99)00165-0.
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  - West, M., et al. “Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series.” Journal of the American Statistical Association, vol. 94, no. 448, Dec. 1999, pp. 1083–95. Scopus, doi:10.1080/01621459.1999.10473861.
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  - West, M., et al. “Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series.” Journal of the American Statistical Association, vol. 94, no. 446, June 1999, pp. 375–87. Scopus, doi:10.1080/01621459.1999.10474128.
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  - Huerta, G., and M. West. “Bayesian inference on periodicities and component spectral structure in time series.” Journal of Time Series Analysis, vol. 20, no. 4, Jan. 1999, pp. 401–16. Scopus, doi:10.1111/1467-9892.00145.
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  - Huerta, G., and M. West. “Priors and component structures in autoregressive time series models.” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 61, no. 4, Jan. 1999, pp. 881–99. Scopus, doi:10.1111/1467-9868.00208.
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  - Krystal, A. D., et al. “New methods of time series analysis for non-stationary EEG data: Eigenstructure decompositions of time varying autoregressions.” Clinical Neurophysiology, vol. 110, 1999, pp. 1–10.
  - West, M. “Time-frequency decompositions: Bayesian model-based approaches.” Conference Record of the Asilomar Conference on Signals, Systems and Computers, vol. 1, Dec. 1998.
  - Tebaldi, C., and M. West. “Rejoinder.” Journal of the American Statistical Association, vol. 93, no. 442, June 1998, pp. 576–576. Scopus, doi:10.1080/01621459.1998.10473710.
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  - Tebaldi, C., and M. West. “Bayesian inference on network traffic using link count data.” Journal of the American Statistical Association, vol. 93, no. 442, June 1998, pp. 557–73. Scopus, doi:10.1080/01621459.1998.10473707.
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  - Tebaldi, Claudia, and Mike West. “Bayesian Inference on Network Traffic Using Link Count Data.” Journal of the American Statistical Association, vol. 93, no. 442, JSTOR, June 1998, pp. 557–557. Crossref, doi:10.2307/2670105.
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  - Huerta, G., and M. West. “Bayesian inference for unequally-spaced time series.” Jsm Proceedings,Section on Bayesian Statistical Science. Alexandria, Va: American Statistical Association, 1998, pp. 17–21.
  - Cargnoni, C., et al. “Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models.” Journal of the American Statistical Association, vol. 92, no. 438, June 1997, pp. 640–47. Scopus, doi:10.1080/01621459.1997.10474015.
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  - West, M. “Hierarchical Mixture Models in Neurological Transmission Analysis.” Journal of the American Statistical Association, vol. 92, no. 438, June 1997, pp. 587–606. Scopus, doi:10.1080/01621459.1997.10474011.
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  - Turner, D. A., et al. “Excitatory synaptic site heterogeneity during paired pulse plasticity in CA1 pyramidal cells in rat hippocampus in vitro..” J Physiol, vol. 500 ( Pt 2), Apr. 1997, pp. 441–61. Pubmed, doi:10.1113/jphysiol.1997.sp022032.
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  - Cao, G., and M. West. “Computing distributions of order statistics.” Communications in Statistics Theory and Methods, vol. 26, no. 3, Jan. 1997, pp. 755–64. Scopus, doi:10.1080/03610929708831947.
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  - Müller, P., et al. “Bayesian models for non-linear autoregressions.” Journal of Time Series Analysis, vol. 18, no. 6, Jan. 1997, pp. 593–614. Scopus, doi:10.1111/1467-9892.00070.
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  - Robert, C. P., et al. “On Bayesian analysis of mixtures with an unknown number of components - Discussion.” Journal of the Royal Statistical Society Series B Statistical Methodology, vol. 59, no. 4, WILEY-BLACKWELL, Jan. 1997, pp. 758–92.
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  - Turner, D. A., et al. “Heterogeneity between non-NMDA synaptic sites in paired-pulse plasticity of CA1 pyramidal neurons in the hippocampus.” Journal of Physiology, vol. 500, 1997, pp. 441–61.
  - West, M. “Time series decomposition.” Biometrika, vol. 84, no. 2, Jan. 1997, pp. 489–94. Scopus, doi:10.1093/biomet/84.2.489.
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  - Cao, G., and M. West. “Practical Bayesian inference using mixtures of mixtures..” Biometrics, vol. 52, no. 4, Dec. 1996, pp. 1334–41. Epmc, doi:10.2307/2532848.
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  - Müller, P., et al. “Bayesian curve fitting using multivariate normal mixtures.” Biometrika, vol. 83, no. 1, Jan. 1996, pp. 67–79. Scopus, doi:10.1093/biomet/83.1.67.
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  - West, M. “Inference in successive sampling discovery models.” Journal of Econometrics, vol. 75, no. 1, Jan. 1996, pp. 217–38. Scopus, doi:10.1016/0304-4076(95)01777-1.
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  - West, Mike. “Bayesian Inference in Cyclical Component Dynamic Linear Models.” Journal of the American Statistical Association, vol. 90, no. 432, JSTOR, Dec. 1995, pp. 1301–1301. Crossref, doi:10.2307/2291520.
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  - Escobar, Michael D., and Mike West. “Bayesian Density Estimation and Inference Using Mixtures.” Journal of the American Statistical Association, vol. 90, no. 430, JSTOR, June 1995, pp. 577–577. Crossref, doi:10.2307/2291069.
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  - Escobar, M. D., and M. West. “Bayesian density estimation and inference using mixtures.” Journal of the American Statistical Association, vol. 90, no. 430, Jan. 1995, pp. 577–88. Scopus, doi:10.1080/01621459.1995.10476550.
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  - West, M. “Bayesian inference in cyclical component dynamic linear models.” Journal of the American Statistical Association, vol. 90, no. 432, Jan. 1995, pp. 1301–12. Scopus, doi:10.1080/01621459.1995.10476634.
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  - West, Mike, and Dennis A. Turner. “Deconvolution of Mixtures in Analysis of Neural Synaptic Transmission.” The Statistician, vol. 43, no. 1, JSTOR, 1994, pp. 31–31. Crossref, doi:10.2307/2348930.
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  - Turner, D. A., and M. West. “Bayesian analysis of mixtures applied to post-synaptic potential fluctuations..” J Neurosci Methods, vol. 47, no. 1–2, Apr. 1993, pp. 1–21. Pubmed, doi:10.1016/0165-0270(93)90017-l.
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  - Turner, D. A., and M. West. “Statistical analysis of mixtures applied to postsynaptic potential fluctuations.” Journal of Neuroscience Methods, vol. 47, 1993, pp. 1–23.
  - WEST, M. “APPROXIMATING POSTERIOR DISTRIBUTIONS BY MIXTURES.” Journal of the Royal Statistical Society Series B Methodological, vol. 55, no. 2, BLACKWELL PUBL LTD, Jan. 1993, pp. 409–22.
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  - West, M. “Approximating posterior distributions by mixtures.” Journal of the Royal Statistical Society (Ser. B), vol. 54, 1993, pp. 553–68.
  - West, M. “Mixture models, Monte Carlo, Bayesian updating and dynamic models.” Computing Science and Statistics, vol. 24, 1993, pp. 325–33.
  - Lavine, M., and M. West. “A Bayesian method for classification and discrimination.” Canadian Journal of Statistics, vol. 20, no. 4, Jan. 1992, pp. 451–61. Scopus, doi:10.2307/3315614.
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  - WEST, M. “MODELING AGENT FORECAST DISTRIBUTIONS.” Journal of the Royal Statistical Society Series B Methodological, vol. 54, no. 2, BLACKWELL PUBL LTD, Jan. 1992, pp. 553–67.
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  - WEST, M., and J. CROSSE. “MODELING PROBABILISTIC AGENT OPINION.” Journal of the Royal Statistical Society Series B Methodological, vol. 54, no. 1, BLACKWELL PUBL LTD, Jan. 1992, pp. 285–99.
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  - Harrison, J., and M. West. “Dynamic linear model diagnostics.” Biometrika, vol. 78, no. 4, Dec. 1991, pp. 797–808. Scopus, doi:10.1093/biomet/78.4.797.
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  - WEST, M., and R. L. WINKLER. “DATA-BASE ERROR TRAPPING AND PREDICTION.” Journal of the American Statistical Association, vol. 86, no. 416, Dec. 1991, pp. 987–96. Wos-lite, doi:10.2307/2290515.
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  - West, M. “Kernel density estimation and marginalization consistency.” Biometrika, vol. 78, no. 2, June 1991, pp. 421–25. Scopus, doi:10.1093/biomet/78.2.421.
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  - Pole, A., and M. West. “Efficient bayesian learning in non‐linear dynamic models.” Journal of Forecasting, vol. 9, no. 2, Jan. 1990, pp. 119–36. Scopus, doi:10.1002/for.3980090205.
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  - Pole, A., and M. West. “REFERENCE ANALYSIS OF THE DYNAMIC LINEAR MODEL.” Journal of Time Series Analysis, vol. 10, no. 2, Jan. 1989, pp. 131–47. Scopus, doi:10.1111/j.1467-9892.1989.tb00020.x.
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  - Pole, A., and M. West. “Reference analysis of the DLM.” Journal of Time Series Analysis, vol. 10, 1989, pp. 131–47.
  - West, M., and J. Harrison. “Subjective intervention in formal models.” Journal of Forecasting, vol. 8, no. 1, Jan. 1989, pp. 33–53. Scopus, doi:10.1002/for.3980080104.
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  - West, Mike. “Bayesian statistics two: Proceedings of the second Valencia international meeting on Bayesian statistics.” International Journal of Forecasting, vol. 4, no. 4, Elsevier BV, Jan. 1988, pp. 609–11. Crossref, doi:10.1016/0169-2070(88)90138-0.
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  - West, M. “On scale mixtures of normal distributions.” Biometrika, vol. 74, no. 3, Sept. 1987, pp. 646–48. Scopus, doi:10.1093/biomet/74.3.646.
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  - GAMERMAN, D., and M. WEST. “AN APPLICATION OF DYNAMIC SURVIVAL MODELS IN UNEMPLOYMENT STUDIES.” Journal of the Royal Statistical Society Series D the Statistician, vol. 36, no. 2–3, BLACKWELL PUBL LTD, Jan. 1987, pp. 269–74.
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  - Gamerman, D., and M. West. “A time series application of dynamic survival models in unemployment studies.” The Statistician, vol. 36, 1987, pp. 269–74.
  - HARRISON, J., and M. WEST. “PRACTICAL BAYESIAN FORECASTING.” Journal of the Royal Statistical Society Series D the Statistician, vol. 36, no. 2–3, BLACKWELL PUBL LTD, Jan. 1987, pp. 115–25.
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  - QUINTANA, J. M., and M. WEST. “AN ANALYSIS OF INTERNATIONAL EXCHANGE-RATES USING MULTIVARIATE DLMS.” Journal of the Royal Statistical Society Series D the Statistician, vol. 36, no. 2–3, BLACKWELL PUBL LTD, Jan. 1987, pp. 275–81.
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  - West, M., et al. “BATS: Bayesian Analysis of Time Series.” The Professional Statistician, vol. 6, 1987, pp. 43–46.
  - West, Mike, and P. Jeff Harrison. “Monitoring and Adaptation in Bayesian Forecasting Models.” Journal of the American Statistical Association, vol. 81, no. 395, JSTOR, Sept. 1986, pp. 741–741. Crossref, doi:10.2307/2289006.
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  - West, Mike. “Discussion: Influence Functionals for Time Series.” The Annals of Statistics, vol. 14, no. 3, Institute of Mathematical Statistics, Sept. 1986, pp. 838–40. Crossref, doi:10.1214/aos/1176350036.
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  - WEST, M. “BAYESIAN MODEL MONITORING.” Journal of the Royal Statistical Society Series B Methodological, vol. 48, no. 1, BLACKWELL PUBL LTD, Jan. 1986, pp. 70–78.
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  - West, M. “Discussion of: Influence functionals for time series.” Annals of Statistics, vol. 14, 1986, pp. 838–40.
  - West, M., and P. J. Harrison. “Monitoring and adaptation in Bayesian forecasting models.” Journal of the American Statistical Association, vol. 81, no. 395, Jan. 1986, pp. 741–50. Scopus, doi:10.1080/01621459.1986.10478331.
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  - West, Mike, et al. “Dynamic Generalized Linear Models and Bayesian Forecasting.” Journal of the American Statistical Association, vol. 80, no. 389, JSTOR, Mar. 1985, pp. 73–73. Crossref, doi:10.2307/2288042.
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  - HARRISON, P. J., and M. WEST. “ASPECTS OF BAYESIAN FORECASTING.” Journal of the Operational Research Society, vol. 36, no. 12, STOCKTON PRESS, Jan. 1985, pp. 1155–1155.
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  - West, M., et al. “Dynamic generalized linear models and Bayesian forecasting.” Journal of the American Statistical Association, vol. 80, no. 389, Jan. 1985, pp. 73–83. Scopus, doi:10.1080/01621459.1985.10477131.
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  - West, M., et al. “Rejoinder.” Journal of the American Statistical Association, vol. 80, no. 389, Jan. 1985, pp. 96–97. Scopus, doi:10.1080/01621459.1985.10477139.
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  - WEST, M. “OUTLIER MODELS AND PRIOR DISTRIBUTIONS IN BAYESIAN LINEAR-REGRESSION.” Journal of the Royal Statistical Society Series B Methodological, vol. 46, no. 3, BLACKWELL PUBL LTD, Jan. 1984, pp. 431–39.
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  - West, Mike. “Bayesian Aggregation.” Journal of the Royal Statistical Society. Series a (General), vol. 147, no. 4, JSTOR, 1984, pp. 600–600. Crossref, doi:10.2307/2981847.
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  - Smith, A. F., and M. West. “Monitoring renal transplants: an application of the multiprocess Kalman filter..” Biometrics, vol. 39, no. 4, Dec. 1983, pp. 867–78. Epmc, doi:10.2307/2531322.
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  - Trimble, I. M., et al. “Detection of renal allograft rejection by computer..” British Medical Journal (Clinical Research Ed.), vol. 286, no. 6379, May 1983, pp. 1695–99. Epmc, doi:10.1136/bmj.286.6379.1695.
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  - SMITH, A. F. M., et al. “MONITORING KIDNEY-TRANSPLANT PATIENTS.” Journal of the Royal Statistical Society Series D the Statistician, vol. 32, no. 1–2, BLACKWELL PUBL LTD, Jan. 1983, pp. 46–54.
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  - Knapp, M. S., et al. “The detection of sudden change in renal function by time-series analysis, including the use of the Kalman Filter - a new statistical approach with potential applications for chronobiologists.” Advances in the Biosciences, vol. 41, no. C, Dec. 1982, pp. 369–73.
  - West, M. “Robust sequential approximate Bayesian estimation.” Journal of the Royal Statistical Society (Ser. B), vol. 43, 1981, pp. 157–66.
  - Bonassi, Fernando V., et al. Bayesian analysis of immune response dynamics with sparse time series data.
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  - Lavine, Isaac, et al. Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models.
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  - West, Mike. “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions.” Annals of the Institute of Statistical Mathematics, Springer Science and Business Media LLC. Crossref, doi:10.1007/s10463-019-00741-3.
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- Book Sections
  - West, M. “Bayesian dynamic modelling.” Bayesian Theory and Applications, edited by P. Damien et al., Clarendon: Oxford University Press, 2013, pp. 145–66.
  - Bernardo, J. M., et al. Preface. Vol. 9780199694587, 2012. Scopus, doi:10.1093/acprof:oso/9780199694587.002.0004.
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  - Merl, D., et al. “Trans-study projection of genomic biomarkers using sparse factor regression models.” The Handbook of Applied Bayesian Analysis, edited by A. O. Hagan and M. West, Oxford University Press, 2010, pp. 118–54.
  - Hyland, R., et al. “Drug-drug interactions: Screening for liability and assessment of risk.” Hit and Lead Profiling: Identification and Optimization of Drug-like Molecules, vol. 43, 2010, pp. 165–96. Scopus, doi:10.1002/9783527627448.ch8.
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  - Lucas, J. E., et al. “In-vitro to In-vivo factor profiling in expression genomics.” Bayesian Modelling in Bioinformatics, edited by D. Dey et al., Taylor-Francis, 2010, pp. p293-316.
  - Shen, H., and M. West. “Bayesian modelling for biological pathway annotation of gene expression pathway signatures.” Frontiers of Statistical Decision Making and Bayesian Analysis, edited by M. -. H. Chen et al., New York: Springer-Verlag, 2010, pp. 285–302.
  - West, M. “Bayesian forecasting.” Methods and Applications of Statistics in Business, Finance and Management Science, edited by N. Balakrishnan, Wiley, 2010, pp. p72-84.
  - Lucas, J. E., et al. “Sparse statistical modelling in gene expression genomics.” Bayesian Inference for Gene Expression and Proteomics, edited by K. A. Do et al., Cambridge University Press, 2006, pp. 155–76.
  - Patil, P. G., et al. “Physiological and statistical approaches to modeling of synaptic responses.” Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics, 2005, pp. 61–88.
  - Liu, J., and M. West. “Combined parameter and state estimation in simulation-based filtering.” Sequential Monte Carlo Methods in Practice, edited by A. Doucet et al., New York: Springer-Verlag, 2001, pp. 197–217.
  - Krystal, A. D., et al. “The spatiotemporal dynamics of generalized tonic-clonic seizure EEG data: Relevance to the climinal practice of electroconvulsive therapy.” Nonlinear Dynamics and Brain Functioning, edited by N. Pradhan et al., New York: Novascience, 1999.
  - Paddock, Susan, et al. “Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design.” Case Studies in Bayesian Statistics, Springer New York, 1999, pp. 339–53. Crossref, doi:10.1007/978-1-4612-1502-8_9.
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  - Turner, D. A., et al. “Statistical inference of synaptic responses: Physiological and statistical approaches.” Modelling in the Neurosciences: From Ion Channels to Neural Networks, edited by R. R. Poznanski, London, Gordon & Breach Publishers, 1999, pp. 39–78.
  - Escobar, M. D., and M. West. “Computing nonparametric hierarchical models.” Practical Non and Semiparametric Bayesian Statistics, edited by P. Müller et al., Springer-Verlag, 1998, pp. 1–16.
  - West, M. “Bayesian forecasting.” Encyclopedia of Statistical Sciences, edited by S. Kotz et al., Wiley, 1998, pp. 50–60.
  - Prado, R., and M. West. “Exploratory modelling of multiple non-stationary time series: Latent process structure and decompositions.” Modelling Longitudinal and Spatially Correlated Data, edited by T. Gregoire, Springer-Verlag, 1997, pp. 349–62.
  - West, M., et al. “Hierarchical priors and mixture models, with application in regression and density estimation.” Aspects of Uncertainty: A Tribute to D.V. Lindley, edited by A. F. M. Smith and P. R. Freeman, London: Wiley, 1994, pp. 363–86.
- Reports
  - Gruber, L., and M. West. GPU-accelerated Bayesian learning and forecasting in simultaneous graphical dynamic linear models. 1 Mar. 2016, pp. 125–49. Scopus, doi:10.1214/15-BA946.
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  - Bonassi, F. V., and M. West. Sequential monte carlo with adaptive weights for approximate bayesian computation. 1 Jan. 2015, pp. 171–87. Scopus, doi:10.1214/14-BA891.
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  - Bonassi, F. V., et al. Bayesian computation for immune response modeling with sparse time series data. 15 May 2014.
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  - Nakajima, J., and M. West. Sparse and dynamic transfer response factor models via Bayesian latent thresholding. June 2013.
  - Fox, E. B., and M. West. Autoregressive models for variance matrices: Stationary inverse Wishart processes. 2013.
  - Lin, L., et al. Discriminative variable subsets in Bayesian classification with mixture models. 2013.
  - Xie, M., and M. West. Dynamic discounting for model mixing in Bayesian forecasting. 2013.
  - Cron, A. J., and M. West. Models of random sparse eigenmatrices matrices with application to Bayesian factor analysis. 2012.
  - Ji, C., et al. Bounded approximations for marginal likelihoods. 2010.
  - Manolopoulou, I., et al. Statistical analysis of immunofluorescent histology. 2010.
  - Merl, D., and M. West. Hierarchical nonparametric mixture models. 2009.
  - Reeson, C., et al. Dynamic graphical models and portfolio allocations for structured mutual funds. 2009.
  - Wang, Q., et al. CellTracer: Software for automated image segmentation and lineage mapping for single-cell studies. 2008.
  - Ferreira, M. A. R., et al. Multi-scale random field models. 2005.
  - West, M. Statistical inference for gravity models in transportation flow forecasting. 1994.
- Conference Papers
  - Cron, A., and M. West. “Models of random sparse eigenmatrices and Bayesian analysis of multivariate structure.” Abel Symposia, vol. 11, 2016, pp. 125–53. Scopus, doi:10.1007/978-3-319-27099-9_7.
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  - Bernardo, J. M., et al. “Bayesian Statistics 9.” Bayesian Statistics 9, vol. 9780199694587, 2012, pp. 1–720. Scopus, doi:10.1093/acprof:oso/9780199694587.001.0001.
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  - Julia, Alongkorn, et al. “The genomic analysis of lactic acidosis and acidosis response in human cancers.” Cancer Research, vol. 69, 2009.
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  - Bild, A. H., et al. “Oncogenic pathway signatures in human cancers as a guide to targeted therapies..” Cancer Epidemiology Biomarkers & Prevention, vol. 14, no. 11, AMER ASSOC CANCER RESEARCH, 2005, pp. 2743S-2743S.
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  - Rich, J., et al. “Gene expression profiling links invasion-related genes to poor survival in older glioblastoma patients.” Neuro Oncology, vol. 7, no. 3, DUKE UNIV PRESS, 2005, pp. 293–293.
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  - Petersen, R. P., et al. “Gene expression signatures for prognosis in NSCLC, coupled with signatures of oncogenic pathway deregulation, provide a novel approach for selection of molecular targets..” Journal of Clinical Oncology, vol. 23, no. 16, AMER SOC CLINICAL ONCOLOGY, 2005, pp. 626S-626S.
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  - Rich, J. N., et al. “Bayesian analysis of gene expression profiles links invasion related genes to poor survival in older glioblastoma patients.” Proceedings of the American Association for Cancer Research, vol. 2005, 2005, pp. 212–212.
  - Berchuck, A., et al. “Prediction of long-term versus short-term survival in advanced stage serous ovarian cancer using expression microarrays..” Journal of the Society for Gynecologic Investigation, vol. 11, no. 2, ELSEVIER SCIENCE INC, 2004, pp. 182A-182A.
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  - Pittman, J. L., et al. “Predictive models that combine multiple forms of genomic and clinical data to achieve personalized prediction of outcomes in breast cancer.” Breast Cancer Research and Treatment, vol. 88, SPRINGER, 2004, pp. S21–22.
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  - Seo, D., et al. “Genomic convergence in the study of human and mouse atherosclerosis.” Circulation, vol. 108, no. 17, LIPPINCOTT WILLIAMS & WILKINS, 2003, pp. 282–282.
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  - Ferreira, M. A. R., et al. “Multiscale modelling of 1-D permeability fields.” Bayesian Statistics 7, edited by J. M. Bernardo et al., Oxford University Press, 2003.
  - West, M. “Bayesian factor regression models in the "Large p, Small n" paradigm.” Bayesian Statistics 7, edited by J. M. Bernardo et al., CLARENDON PRESS, 2003, pp. 733–42.
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  - West, M. Case Studies in Bayesian Statistics V. Edited by C. A. Gatsonis et al., Springer-Verlag, New York, 2001.
  - Aguilar, O., and M. West. “Analysis of hospital quality monitors using hierarchical time series models.” Case Studies in Bayesian Statistics, Vol. 4, edited by C. A. Gatsonis et al., Springer Verlag, New York, 1999, pp. 287–302.
  - Aguilar, O., et al. “Bayesian inference on latent structure in time series.” Bayesian Statistics 6, edited by J. M. Bernardo et al., OXFORD UNIV PRESS, 1999, pp. 3–26.
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  - Paddock, S., et al. “Mixture models in the exploration of structure-activity relationships in drug design.” Case Studies in Bayesian Statistics, Vol. 4, edited by C. A. Gatsonis et al., Springer-Verlag, New York, 1998, pp. 339–54.
  - West, M. Case Studies in Bayesian Statistics IV. Edited by C. A. Gatsonis et al., Springer-Verlag, New York, 1998.
  - West, M. “Some statistical issues in Palæoclimatology (with discussion).” Bayesian Statistics 5, edited by J. M. Bernardo et al., Oxford University Press, 1996, pp. 461–86.
  - West, M. “Bayesian time series: Models and computations for the analysis of time series in the physical sciences.” Maximum Entropy and Bayesian Methods, edited by K. M. Hanson and R. N. Silver, vol. 79, KLUWER ACADEMIC PUBL, 1996, pp. 23–34.
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  - West, Mike. Modelling and robustness issues in Bayesian time series analysis. Institute of Mathematical Statistics, 1996, pp. 231–52. Crossref, doi:10.1214/lnms/1215453070.
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  - West, M. “Discovery sampling and selection models.” Statistical Decision Theory and Related Topics, edited by S. S. Gupta and J. O. Berger, Springer-Verlag, New York, 1994, pp. 221–35.
  - West, M., and G. Cao. “Assessing mechanisms of neural synaptic activity.” Case Studies in Bayesian Statistics, Vol. 1, edited by C. A. Gatsonis et al., Springer-Verlag, New York, 1993, pp. 416–28.
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