Book section · 2015
This article provides an overview of experimental design using a Bayesian decision-theoretic framework. Scientific experimentation requires decisions about how an experiment will be conducted and analyzed. Such decisions depend on the goals and purpose of ...
Full textLink to itemCite
Book section · 2007
We consider the nonparametric regression problem of estimating an unknown function based on noisy data. One approach to this estimation problem is to represent the function in a series expansion using a linear combination of basis functions. Overcomplete d ...
Cite
Book section · 2006
We present model-based inference for proteomic peak identification and quantification from mass spectroscopy data, focusing on nonparametric Bayesian models. Using experimental data generated from MALDI-TOF mass spectroscopy (matrix-assisted laser desorpti ...
Full textCite
Book section · 2001
This article provides an overview of experimental design using a Bayesian decision-theoretic framework. Scientific experimentation requires decisions about how an experiment will be conducted and analyzed. Such decisions depend on the goals and purpose of ...
Full textLink to itemCite
Book section · 1999
Bayesian methods based on hierarchical mixture models have demonstrated excellent mean squared error properties in constructing data dependent shrinkage estimators in wavelets, however, subjective elicitation of the hyperparameters is challenging. In this ...
Full textLink to itemCite
Book section · 1998
We report on a study of mixture modeling problems arising in the assessment of chemical structure-activity relationships in drug design and discovery. Pharmaceutical research laboratories developing test compounds for screening synthesize many related cand ...
Full textCite
Book section · 1996
This chapter focuses on Bayesian inference and design in binary regression experiments . As a case
study we consider heart de brillator experiments in which the number of observations that can be
taken is limited and it is important to incorporate all a ...
Link to itemCite
Book section · 1996
Prediction methods based on mixing over a set of plausible models can help alleviate the sensitivity of inference and decisions to modeling assumptions. One important application area is prediction in linear models. Computing techniques for model mixing in ...
Full textCite
Book section · 1995
In many experimental design problems, the primary interest is in estimating functions of the parameters and a design is selected according to some optimality criterion. The assumption that parameter estimates are approximately normally distributed is often ...
Full textLink to itemCite
Book section · 1995
During heart defibrillator implantation, a physician fibrillates the patient’s heart several times at different test strengths to estimate the effective strength necessary for defibrillation. One strategy is to implant at the strength that de-fibrillates 9 ...
Full textLink to itemCite
Book section · 1991
The spatial models considered in this paper are Gibbs processes with pairwise interaction potentials, which provide a rich framework for models where the likelihood of a particular configuration of points depends on attraction or repulsion between neighbor ...
Full textCite
https://orcid.org/0000-0002-3595-1872