Bayesian statistics for beginners : a step-by-step approach / Therese M. Donovan, Ruth M. Mickey.

By:

Donovan, Therese M. (Therese Marie) [author.]

Contributor(s):

Mickey, Ruth M, 1954- [author.]

Material type:

TextPublisher: Oxford, United Kingdom : Oxford University Press, 2019Edition: First editionDescription: x, 419 pages : illustrations (chiefly color) ; 26 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780198841302

Subject(s):

Bayesian statistical decision theory

DDC classification:

519.542 LUN

Contents:

Section 1 Basics of Probability 1: Introduction to Probability 2: Joint, Marginal, and Conditional Probability Section 2 Bayes' Theorem and Bayesian Inference 3: Bayes' Theorem 4: Bayesian Inference 5: The Author Problem - Bayesian Inference with Two Hypotheses 6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses 7: The Portrait Problem: Bayesian Inference with Joint Likelihood Section 3 Probability Functions 8: Probability Mass Functions 9: Probability Density Functions Section 4 Bayesian Conjugates 10: The White House Problem: The Beta-Binomial Conjugate 11: The Shark Attack Problem: The Gamma-Poisson Conjugate 12: The Maple Syrup Problem: The Normal-Normal Conjugate Section 5 Markov Chain Monte Carlo 13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm 14: MCMC Diagnostic Approaches 15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm 16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling Section 6 Applications 17: The Survivor Problem: Simple Linear Regression with MCMC 18: The Survivor Problem Continued: Introduction to Bayesian Model Selection 19: The Lorax Problem: Introduction to Bayesian Networks 20: The Once-ler Problem: Introduction to Decision Trees Appendices Appendix 1: The Beta-Binomial Conjugate Solution Appendix 2: The Gamma-Poisson Conjugate Solution Appendix 3: The Normal-Normal Conjugate Solution Appendix 4: Conjugate Solutions for Simple Linear Regression Appendix 5: The Standardization of Regression Data

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Reference text books	All India Institute of Speech and Hearing		R-21/2		519.542 LUN (Browse shelf(Opens below))					Available			23266

Total holds: 0

Includes bibliographical references (pages 399-401) and indexes.

Section 1
Basics of Probability
1: Introduction to Probability
2: Joint, Marginal, and Conditional Probability
Section 2
Bayes' Theorem and Bayesian Inference
3: Bayes' Theorem
4: Bayesian Inference
5: The Author Problem - Bayesian Inference with Two Hypotheses
6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses
7: The Portrait Problem: Bayesian Inference with Joint Likelihood
Section 3
Probability Functions
8: Probability Mass Functions
9: Probability Density Functions
Section 4
Bayesian Conjugates
10: The White House Problem: The Beta-Binomial Conjugate
11: The Shark Attack Problem: The Gamma-Poisson Conjugate
12: The Maple Syrup Problem: The Normal-Normal Conjugate
Section 5
Markov Chain Monte Carlo
13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm
14: MCMC Diagnostic Approaches
15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm
16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
Section 6
Applications
17: The Survivor Problem: Simple Linear Regression with MCMC
18: The Survivor Problem Continued: Introduction to Bayesian Model Selection
19: The Lorax Problem: Introduction to Bayesian Networks
20: The Once-ler Problem: Introduction to Decision Trees
Appendices
Appendix 1: The Beta-Binomial Conjugate Solution
Appendix 2: The Gamma-Poisson Conjugate Solution
Appendix 3: The Normal-Normal Conjugate Solution
Appendix 4: Conjugate Solutions for Simple Linear Regression
Appendix 5: The Standardization of Regression Data

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