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Graphs and stochastic relaxation for hierarchical bayes modelling
Statistics in Medicine, 1992AbstractThis expository paper describes two useful tools for the statistical analysis of processes that generate repeated measures and longitudinal data. The first tool is a graph for a visual description of dependency structures. The second tool is a stochastic relaxation method (‘Gibbs sampling’) for fitting hierarchical Bayes models.
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Hierarchical and Empirical Bayes Extensions
1994In 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, 2004Abstract 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, 1999This 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, 1994Hierarchical 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 +4 more
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Hierarchical naive bayes models for representing user profiles
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, 2008In this paper, we show how a user profile can be enhanced when a more detailed description of the products is included. Two main assumptions have been considered: the first implies that the set of features used to describe an item can be organized into a well-defined set of components or categories, and the second is that the user's rating for a given ...
Juan F. Huete +3 more
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Variational Bayes for Hierarchical Mixture Models
2018In 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 +2 more
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On Hierarchical Bayes Procedures for Predicting Simple Exponential Survival
Biometrics, 1990The 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
2014In 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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