Results 261 to 270 of about 635,362 (294)

Hierarchical and Empirical Bayes Extensions

1994
In the previous chapters, we have noticed the ambivalent aspect of Bayesian analysis: It is sufficiently reducing to produce an effective decision, but this efficiency can also be misused. For instance, the subjective aspects of Bayesian analysis can always be modified so that it produces conclusions fixed in advance.
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Expanded uncertainty quantification in inverse problems: Hierarchical Bayes and empirical Bayes

Geophysics, 2004
Abstract A common way to account for uncertainty in inverse problems is to apply Bayes' rule and obtain a posterior distribution of the quantities of interest given a set of measurements. A conventional Bayesian treatment, however, requires assuming specific values for parameters of the prior distribution and of the distribution of ...
Alberto Malinverno, Victoria A. Briggs
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Criticism of a hierarchical model using Bayes factors

Statistics in Medicine, 1999
This paper analyses a data file of heart transplant surgeries performed in the United States over a two-year period. A Poisson/gamma exchangeable model is used to learn about the underlying death rates for 94 hospitals. There are concerns about the suitability of this hierarchical model, including the need for a hierarchical structure, the existence of
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An Investigation of Hierarchical Bayes Procedures in Item Response Theory

Psychometrika, 1994
Hierarchical Bayes procedures for the two-parameter logistic item response model were compared for estimating item and ability parameters. Simulated data sets were analyzed via two joint and two marginal Bayesian estimation procedures. The marginal Bayesian estimation procedures yielded consistently smaller root mean square differences than the joint ...
Kim, Seock-Ho, Cohen, Allan S., Baker, Frank B., Subkoviak, Michael J., Leonard, Tom   +4 more
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Hierarchical Bayes Models: A Practitioners Guide

2005
Hierarchical Bayes models free researchers from computational constraints and allow for the development of more realistic models of buyer behavior and decision making. Moreover, this freedom enables exploration of marketing problems that have proven elusive over the years, such as models for advertising ROI, sales force effectiveness, and similarly ...
Greg M. Allenby, Peter E. Rossi, Robert E. McCulloch   +2 more
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A hierarchical Naïve Bayes model for approximate identity matching

Decision Support Systems, 2011
Organizations often manage identity information for their customers, vendors, and employees. Identity management is critical to various organizational practices ranging from customer relationship management to crime investigation. The task of searching for a specific identity is difficult because disparate identity information may exist due to the ...
G. Alan Wang, Homa Atabakhsh, Hsinchun Chen   +2 more
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Variational Bayes for Hierarchical Mixture Models

2018
In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter sparsity is substantial. Classification with finite mixture models is based on the posterior expectation of latent indicator variables.
Muting Wan, James G. Booth, Martin T. Wells   +2 more
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On Hierarchical Bayes Procedures for Predicting Simple Exponential Survival

Biometrics, 1990
The situation considered is the prediction of a future observation from a simple exponential survival distribution in a hierarchical Bayes context. It is shown that when the hyperparameters need to be estimated from the data, a sample reuse approach is superior to maximum likelihood and method of moments estimation procedures.
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Hierarchical Bayes Analysis of Behavioral Experiments

2014
In this dissertation, we develop generalized hierarchical Bayesian ANOVA, to assist experimental researchers in the behavioral and social sciences in the analysis of the effects of experimentally manipulated within- and between-subjects factors. The method alleviates several limitations of classical ANOVA, still commonly employed in those fields.
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hierarchical Bayes models

2008
Siddhartha Chib, Edward Greenberg
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