A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Dipak K. Dey is Head and Professor of Statistics at the University of Connecticut.
Inhaltsangabe
I Dirichlet and Related Processes.- 1 Computing Nonparametric Hierarchical Models.- 2 Computational Methods for Mixture of Dirichlet Process Models.- 3 Nonparametric Bayes Methods Using Predictive Updating.- 4 Dynamic Display of Changing Posterior in Bayesian Survival Analysis.- 5 Semiparametric Bayesian Methods for Random Effects Models.- 6 Nonparametric Bayesian Group Sequential Design.- II Modeling Random Functions.- 7 Wavelet-Based Nonparametric Bayes Methods.- 8 Nonparametric Estimation of Irregular Functions with Independent or Autocorrelated Errors.- 9 Feedforward Neural Networks for Nonparametric Regression.- III Levy and Related Processes.- 10 Survival Analysis Using Semiparametric Bayesian Methods.- 11 Bayesian Nonparametric and Covariate Analysis of Failure Time Data.- 12 Simulation of Lévy Random Fields.- 13 Sampling Methods for Bayesian Nonparametric Inference Involving Stochastic Processes.- 14 Curve and Surface Estimation Using Dynamic Step Functions.- IV Prior Elicitation and Asymptotic Properties 15 Prior Elicitation for Semiparametric Bayesian Survival Analysis.- 16 Asymptotic Properties of Nonparametric Bayesian Procedures.- 17 Modeling Travel Demand in Portland, Oregon.- 18 Semiparametric PK/PD Models.- 19 A Bayesian Model for Fatigue Crack Growth.- 20 A Semiparametric Model for Labor Earnings Dynamics.
I Dirichlet and Related Processes.- 1 Computing Nonparametric Hierarchical Models.- 2 Computational Methods for Mixture of Dirichlet Process Models.- 3 Nonparametric Bayes Methods Using Predictive Updating.- 4 Dynamic Display of Changing Posterior in Bayesian Survival Analysis.- 5 Semiparametric Bayesian Methods for Random Effects Models.- 6 Nonparametric Bayesian Group Sequential Design.- II Modeling Random Functions.- 7 Wavelet-Based Nonparametric Bayes Methods.- 8 Nonparametric Estimation of Irregular Functions with Independent or Autocorrelated Errors.- 9 Feedforward Neural Networks for Nonparametric Regression.- III Levy and Related Processes.- 10 Survival Analysis Using Semiparametric Bayesian Methods.- 11 Bayesian Nonparametric and Covariate Analysis of Failure Time Data.- 12 Simulation of Lévy Random Fields.- 13 Sampling Methods for Bayesian Nonparametric Inference Involving Stochastic Processes.- 14 Curve and Surface Estimation Using Dynamic Step Functions.- IV Prior Elicitation and Asymptotic Properties 15 Prior Elicitation for Semiparametric Bayesian Survival Analysis.- 16 Asymptotic Properties of Nonparametric Bayesian Procedures.- 17 Modeling Travel Demand in Portland, Oregon.- 18 Semiparametric PK/PD Models.- 19 A Bayesian Model for Fatigue Crack Growth.- 20 A Semiparametric Model for Labor Earnings Dynamics.
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