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Markov-modulated marked poisson processes for check-in data

Publication ,  Conference
Pan, J; Rao, V; Agarwal, PK; Gelfand, AE
Published in: 33rd International Conference on Machine Learning, ICML 2016
January 1, 2016

We develop continuous-time probabilistic models to study trajectory data consisting of times and locations of user 'check-ins'. We model the data as realizations of a marked point process, with intensity and mark-distribution modulated by a latent Markov jump process (MJP). We also include user-heterogeneity in our model by assigning each user a vector of 'preferred locations'. Our model extends latent Dirichlet allocation by dropping the bag-of-words assumption and operating in continuous time. We show how an appropriate choice of priors allows efficient posterior inference. Our experiments demonstrate the usefulness of our approach by comparing with various baselines on a variety of tasks.copyright

Duke Scholars

Published In

33rd International Conference on Machine Learning, ICML 2016

ISBN

9781510829008

Publication Date

January 1, 2016

Volume

5

Start / End Page

3311 / 3320
 

Citation

APA
Chicago
ICMJE
MLA
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Pan, J., Rao, V., Agarwal, P. K., & Gelfand, A. E. (2016). Markov-modulated marked poisson processes for check-in data. In 33rd International Conference on Machine Learning, ICML 2016 (Vol. 5, pp. 3311–3320).
Pan, J., V. Rao, P. K. Agarwal, and A. E. Gelfand. “Markov-modulated marked poisson processes for check-in data.” In 33rd International Conference on Machine Learning, ICML 2016, 5:3311–20, 2016.
Pan J, Rao V, Agarwal PK, Gelfand AE. Markov-modulated marked poisson processes for check-in data. In: 33rd International Conference on Machine Learning, ICML 2016. 2016. p. 3311–20.
Pan, J., et al. “Markov-modulated marked poisson processes for check-in data.” 33rd International Conference on Machine Learning, ICML 2016, vol. 5, 2016, pp. 3311–20.
Pan J, Rao V, Agarwal PK, Gelfand AE. Markov-modulated marked poisson processes for check-in data. 33rd International Conference on Machine Learning, ICML 2016. 2016. p. 3311–3320.

Published In

33rd International Conference on Machine Learning, ICML 2016

ISBN

9781510829008

Publication Date

January 1, 2016

Volume

5

Start / End Page

3311 / 3320