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Bayesian Statistics for Beginners: A Step-by-Step Approach provides the student a detailed overview of Bayesian statistics concepts, including Bayes’ Theorem, Bayesian inference, Bayesian inference with two hypotheses, probability mass functions, the beta-binomial conjugate, the normal-normal conjugate, the Gamma-Poissan conjugate, MCMC with the Metropolis algorithm, among others. This textbook is direct and concise with systematic content designed to engage today’s student through visually engaging content. There is a wide variety of assignment types that help students master Bayesian statistics concepts & build real-world working capability. Bayesian Statistics for Beginners: A Step-by-Step Approach goes into MCMC with Gibbs Sampling, simple linear regression with MCMC, introduction to Bayesian networks, introduction to decision trees, introduction to Bayesian model selection, and the basics of probability. A chapter is broken down into Markov Chain Monte Carlo. Buy the textbook Bayesian Statistics for Beginners: A Step-by-Step Approach and be confident in your Bayesian statistics education and career.
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