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📙 Handbook of Statistics 25: Bayesian Thinking: Modeling and Computation by Dipak K. Dey, C.R. Rao — free pdf


This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians.

Key Features:

- Critical thinking on causal effects - Objective Bayesian philosophy - Nonparametric Bayesian methodology - Simulation based computing techniques - Bioinformatics and Biostatistics Key Features:

· Critical thinking on causal effects · Objective Bayesian philosophy · Nonparametric Bayesian methodology · Simulation based computing techniques · Bioinformatics and Biostatistics

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