Bayesian statistics: an introduction
This new edition of Lee's popular book introduces the Bayesian philosophy of statistics. It has been completely updated and features new chapters on Gibbs sampling and hierarchical methods and more exercises.
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Bayesian Inference for the Normal Distribution
Some Other Common Distributions
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algorithm Appendix approximation Bayes factor Bayesian beta distribution binomial distribution bivariate Cauchy distribution chi-squared distribution classical statisticians conjugate family conjugate prior consider constant continuous defined degrees of freedom denoted density function depends discrete distribution function empirical Bayes equivalently estimator example exponential family follows formula Gibbs sampler given hence hierachical hyperparameters independent inference integral interval iteration Jeffreys known likelihood principle linear loss function matrix mean and variance median method mode normal distribution normal prior normal variance notation null hypothesis observations Pareto distribution particular Poisson distribution possible posterior density posterior distribution posterior mean posterior probability predictive distribution prior beliefs prior density prior distribution prior information problem random variables reasonable reference prior regression result sample standard deviation stopping rule sufficient statistic Suppose Table B.5 theorem uniform distribution uniform prior unknown parameter usually vector write