A First Course in Bayesian Statistical Methods

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introduction staistics
Contents
1  
Belief probability and exchangeability  13 
Oneparameter models  31 
Monte Carlo approximation  53 
The normal model  67 
Posterior approximation with the Gibbs sampler  88 
The multivariate normal model  105 
Group comparisons and hierarchical modeling  125 
Linear regression  148 
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Common terms and phrases
algorithm analysis approximation average Bayes Bayesian beliefs calculate called Chapter coefficients compute confidence confidence interval conjugate contains correlation counties covariance dataset density depend described discuss effects equal estimate example Exercise expectation full conditional distribution function Gibbs sampler given gives hierarchical independent indicates inference interest iteration joint linear marginal Markov chain matrix MCMC measure methods Metropolis missing Monte Carlo multivariate normal normal distribution normal model observed obtained parameters plot Poisson population population mean posterior distribution precision predictive predictive distribution prior distribution probability procedure proposal provides quantities random variable reasonable references regression model relationship represents sample mean sampling model says score sequence shows squares standard statistical suggests Suppose theta true unknown values variance vector zero