Introduction to Bayesian Statistics

ISBN-10: 0470141158

ISBN-13: 9780470141151

Edition: 2nd 2007 (Revised)

Authors: William M. Bolstad

List price: $160.00
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Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. This book uniquely covers the topics usually found in a typical introductory statistics book, but from a Bayesian perspective.
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Book details

List price: $160.00
Edition: 2nd
Copyright year: 2007
Publisher: John Wiley & Sons, Incorporated
Publication date: 8/15/2007
Binding: Hardcover
Pages: 464
Size: 6.00" wide x 9.25" long x 1.00" tall
Weight: 1.914
Language: English

William M. Bolstad, PhD, is Senior Lecturer in the Department of Statistics at The University of Waikato, New Zealand. He holds degrees from the University of Missouri, Stanford University, and The University of Waikato. Dr. Bolstad's research interests include Bayesian statistics, MCMC methods, recursive estimation techniques, multiprocess dynamic time series models, and forecasting.

Preface to First Edition
Introduction to Statistical Science
The Scientific Method: A Process for Learning
The Role of Statistics in the Scientific Method
Main Approaches to Statistics
Purpose and Organization of This Text
Scientific Data Gathering
Sampling from a Real Population
Observational Studies and Designed Experiments
Monte Carlo Exercises
Displaying and Summarizing Data
Graphically Displaying a Single Variable
Graphically Comparing Two Samples
Measures of Location
Measures of Spread
Displaying Relationships Between Two or More Variables
Measures of Association for Two or More Variables
Logic, Probability, and Uncertainty
Deductive Logic and Plausible Reasoning
Axioms of Probability
Joint Probability and Independent Events
Conditional Probability
Bayes' Theorem
Assigning Probabilities
Odds Ratios and Bayes Factor
Beat the Dealer
Discrete Random Variables
Discrete Random Variables
Probability Distribution of a Discrete Random Variable
Binomial Distribution
Hypergeometric Distribution
Poisson Distribution
Joint Random Variables
Conditional Probability for Joint Random Variables
Bayesian Inference for Discrete Random Variables
Two Equivalent Ways of Using Bayes' Theorem
Bayes' Theorem for Binomial with Discrete Prior
Important Consequences of Bayes' Theorem
Bayes' theorem for Poisson with Discrete Prior
Computer Exercises
Continuous Random Variables
Probability Density Function
Some Continuous Distributions
Joint Continuous Random Variables
Joint Continuous and Discrete Random Variables
Bayesian Inference for Binomial Proportion
Using a Uniform Prior
Using a Beta Prior
Choosing Your Prior
Summarizing the Posterior Distribution
Estimating the Proportion
Bayesian Credible Interval
Computer Exercises
Comparing Bayesian and Frequentist Inferences for Proportion
Frequentist Interpretation of Probability and Parameters
Point Estimation
Comparing Estimators for Proportion
Interval Estimation
Hypothesis Testing
Testing a OneSided Hypothesis
Testing a TwoSided Hypothesis
Carlo Exercises
Bayesian Inference for Poisson
Some Prior Distributions for Poisson
Inference for Poisson Parameter
Computer Exercises
Bayesian Inference for Normal Mean
Bayes' Theorem for Normal Mean with a Discrete Prior
Bayes' Theorem for Normal Mean with a Continuous Prior
Choosing Your Normal Prior
Bayesian Credible Interval for Normal Mean
Predictive Density for Next Observation
Computer Exercises
Comparing Bayesian and Frequentist Inferences for Mean
Comparing Frequentist and Bayesian Point Estimators
Comparing Confidence and Credible Intervals for Mean
Testing a OneSided Hypothesis about a Normal Mean
Testing a TwoSided Hypothesis about a Normal Mean
Bayesian Infer
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