![]() ![]() 4 illustrates the essence of MCMC through the simple example of the Metropolis algorithm. 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap. This book aims to equip readers with a sound understanding of MCMC and enable them to write simulation codes by themselves. Bayesian statistics, quantum physics, machine learning, computer science, computational biology, and mathematical economics. MCMC is frequently used in diverse fields where statistical methods are important - e.g. MCMC is a powerful technique that can be used to integrate complicated functions or to handle complicated probability distributions. ![]() This textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming.
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