Move beyond predictions and learn how to build machine learning systems that represent uncertainty, update intelligently, and support better decisions. The Bayesian Machine Learning Handbook gives you a structured path from essential probability concepts to advanced probabilistic models. Whether you are a student, researcher, data scientist, or machine learning practitioner, this comprehensive reference helps you understand both the reasoning and mathematics behind modern Bayesian methods. Inside, you will learn how to: Apply Bayes’ theorem, likelihoods, priors, and posterior distributions Build Bayesian linear, logistic, hierarchical, and graphical models Master MCMC, Gibbs sampling, Hamiltonian Monte Carlo, and NUTS Understand variational inference, Laplace approximation, and expectation-maximization Evaluate models using posterior predictive checks, Bayes factors, WAIC, and cross-validation Work with mixture models, Gaussian processes, and Bayesian nonparametrics Explore Bayesian neural networks, deep generative models, optimization, and bandit methods Connect probabilistic programming techniques to real-world applications in science, engineering, medicine, finance, and artificial intelligence Organized into five progressive parts and fifteen detailed chapters, the handbook balances mathematical foundations with worked examples, practical guidance, model diagnostics, and implementation considerations. Its extensive glossary and research-based references also make it a dependable resource for continued study. Instead of treating uncertainty as an inconvenience, you will learn to model it honestly and use it as a powerful source of insight. Strengthen your command of probabilistic machine learning—order your copy today.
