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Once again, I'm impressed with the level of interaction that is made almost daily from the instructors and support team. Thank you for all your help. View on Wiley Online Library. This is a dummy description. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach.
Topics of coverage include: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution The distributions from the one-dimensional exponential family Markov chains and their long-run behavior The Metropolis-Hastings algorithm Gibbs sampling algorithm and methods for speeding up convergence Markov chain Monte Carlo sampling Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics.
Understanding Computational Bayesian Statistics | Wiley
About the Author William M. Bolstad's research interests include Bayesian statistics, MCMC methods, recursive estimation techniques, multiprocess dynamic time series models, and forecasting.
arialuxuryapulia.com/176.php Bayesian Inference 47 3. Bayesian Statistics Using Conjugate Priors 61 4. Markov Chains 5. Reviews "Understanding computational Bayesian statistics is an excellent book for courses on computational statistics at the advanced undergraduate and graduate levels. Extra Related Web Site.