Description
Scalable Monte Carlo for Bayesian Learning is a definitive guide to modern computational methods for Bayesian statistics. Written by leading experts in the field, this monograph explores advanced Monte Carlo techniques designed to handle large-scale datasets and complex probabilistic models.
The book covers essential topics including Markov Chain Monte Carlo (MCMC), Hamiltonian dynamics, variational inference, and particle methods. It provides both theoretical foundations and practical implementations for practitioners working with big data and high-dimensional problems. The text emphasizes scalability, addressing the computational challenges that arise when traditional Bayesian methods are applied to modern datasets.
Ideal for statisticians, machine learning practitioners, and researchers in mathematical sciences, this monograph offers a rigorous treatment of state-of-the-art techniques with real-world applications and algorithmic insights.
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