Bayesian Statistics with Python provides a comprehensive exploration of Bayesian statistical theory and practical applications. The content spans a broad range of topics, beginning with foundational concepts in probability theory and Bayesian inference, including key issues like prior and posterior distributions, likelihood functions, and Markov Chain Monte Carlo (MCMC) methods. It progresses into more advanced topics such as hierarchical modeling, Bayesian model comparison, and Bayesian time series analysis. The book also covers specialized areas of Bayesian statistics like Bayesian networks, Gaussian processes, and variational inference, ensuring that readers gain both a theoretical understanding and practical insight into applying Bayesian methods in various contexts.