- 1,062 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
About this book
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.- Critical thinking on causal effects- Objective Bayesian philosophy- Nonparametric Bayesian methodology- Simulation based computing techniques- Bioinformatics and Biostatistics
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Yes, you can access Bayesian Thinking, Modeling and Computation by in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Preface
- Table of contents
- Contributors
- Bayesian Inference for Causal Effects
- Reference Analysis
- Probability Matching Priors
- Model Selection and Hypothesis Testing based on Objective Probabilities and Bayes Factors
- Role of P-values and other Measures of Evidence in Bayesian Analysis
- Bayesian Model Checking and Model Diagnostics
- The Elimination of Nuisance Parameters
- Bayesian Estimation of Multivariate Location Parameters
- Bayesian Nonparametric Modeling and Data Analysis: An Introduction
- Some Bayesian Nonparametric Models
- Bayesian Modeling in the Wavelet Domain
- Bayesian Nonparametric Inference
- Bayesian Methods for Function Estimation
- MCMC Methods to Estimate Bayesian Parametric Models
- Bayesian Computation: From Posterior Densities to Bayes Factors, Marginal Likelihoods, and Posterior Model Probabilities
- Bayesian Modelling and Inference on Mixtures of Distributions
- Simulation Based Optimal Design
- Variable Selection and Covariance Selection in Multivariate Regression Models
- Dynamic Models
- Bayesian Thinking in Spatial Statistics
- Robust Bayesian Analysis
- Elliptical Measurement Error Models - A Bayesian Approach
- Bayesian Sensitivity Analysis in Skew-elliptical Models
- Bayesian Methods for DNA Microarray Data Analysis
- Bayesian Biostatistics
- Innovative Bayesian Methods for Biostatistics and Epidemiology
- Bayesian Analysis of Case-Control Studies
- Bayesian Analysis of ROC Data
- Modeling and Analysis for Categorical Response Data
- Bayesian Methods and Simulation-Based Computation for Contingency Tables
- Multiple Events Time Data: A Bayesian Recourse
- Bayesian Survival Analysis for Discrete Data with Left-Truncation and Interval Censoring
- Software Reliability
- Bayesian Aspects of Small Areasmall area Estimation
- Teaching Bayesian Thought to Nonstatisticians
- Colour figures
- Subject Index
- Contents of Previous Volumes