Tabla de Contenidos: “…9.3.1 Bayesian Sufficiency and the Principle -- 9.3.2 Bayesian Analysis and Likelihood Principle -- 9.3.3 Informative and Conjugate Prior -- 9.3.4 Non-informative Prior -- 9.4 Bayesian Estimation -- 9.4.1 Inference for Binomial Distribution -- 9.4.2 Inference for the Poisson Distribution -- 9.4.3 Inference for Uniform Distribution -- 9.4.4 Inference for Exponential Distribution -- 9.4.5 Inference for Normal Distributions -- 9.5 The Credible Intervals -- 9.6 Bayes Factors for Testing Problems -- 9.7 Further Reading -- 9.8 Complements, Problems, and Programs -- Part III Stochastic Processes and Monte Carlo -- Chapter 10 Stochastic Processes -- 10.1 Introduction -- 10.2 Kolmogorov's Consistency Theorem -- 10.3 Markov Chains -- 10.3.1 The m-Step TPM -- 10.3.2 Classification of States -- 10.3.3 Canonical Decomposition of an Absorbing Markov Chain -- 10.3.4 Stationary Distribution and Mean First Passage Time of an Ergodic Markov Chain -- 10.3.5 Time Reversible Markov Chain -- 10.4 Application of Markov Chains in Computational Statistics -- 10.4.1 The Metropolis-Hastings Algorithm -- 10.4.2 Gibbs Sampler -- 10.4.3 Illustrative Examples -- 10.5 Further Reading -- 10.6 Complements, Problems, and Programs -- Chapter 11 Monte Carlo Computations -- 11.1 Introduction -- 11.2 Generating the (Pseudo-) Random Numbers -- 11.2.1 Useful Random Generators -- 11.2.2 Probability Through Simulation -- 11.3 Simulation from Probability Distributions and Some Limit Theorems -- 11.3.1 Simulation from Discrete Distributions -- 11.3.2 Simulation from Continuous Distributions -- 11.3.3 Understanding Limit Theorems through Simulation -- 11.3.4 Understanding The Central Limit Theorem -- 11.4 Monte Carlo Integration -- 11.5 The Accept-Reject Technique -- 11.6 Application to Bayesian Inference -- 11.7 Further Reading -- 11.8 Complements, Problems, and Programs…”

Tabla de Contenidos: “…Posterior Sampling -- 1.5.5.1. The Metropolis-Hastings Algorithm -- 1.5.5.2. Gibbs Sampling -- 1.5.5.3. …”