Markov Chain Monte Carlo, Bayesian Inference, Computational Statistics, Genetic Data Analysis
Abstract
Markov Chain Monte Carlo (MCMC) methods represent a significant advancement in computational statistics, offering powerful tools for solving complex problems involving high-dimensional probability distributions. This paper provides a comprehensive overview of the theoretical foundations and practical applications of MCMC methods. This paper begins by discussing the fundamental principles of Markov chains and Monte Carlo simulations, highlighting how their combination facilitates the estimation and how it use for solving of complex integrals and optimization problems. The paper further explores various applications of MCMC in fields such as finance, computer science, and biology, including risk management, Bayesian inference, and genetic data analysis. Despite their extensive use, MCMC methods face challenges related to convergence and computational efficiency, which are addressed through ongoing advancements in algorithmic techniques and computational resources. This overview aims to elucidate the core principles and practical relevance of MCMC methods, offering insights into their applications and encouraging future research in this dynamic area.
