A01=Laziz U Abdullaev
A01=Utkir A Rozikov
Artificial Intelligence
Author_Laziz U Abdullaev
Author_Utkir A Rozikov
Bayesian Inference
Category=PBT
Category=UYQM
Computational Complexity
Data Analysis
Decision Making
Deep Learning
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Gibbs Measures
Graphical Models
Information Theory
Machine Learning
Markov Random Fields
Neural Networks
Optimization Algorithms
Pattern Recognition
Probability Theory
Reinforcement Learning
Statistical Learning
Statistical Mechanics
Stochastic Processes
Product details
- ISBN 9789819814565
- Publication Date: 12 Jan 2026
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
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From the Ising model to large language models, Gibbs Measures in Machine Learning offers a complete journey through one of the most powerful concepts connecting statistical physics and modern AI.Starting with the mathematical foundations — measure theory, Markov chains, and configuration spaces — the book builds toward advanced applications in Bayesian inference, structured prediction, unsupervised learning, and deep neural networks. Along the way, it bridges classical models such as Potts and Solid-on-Solid with state-of-the-art techniques like attention mechanisms, diffusion models, and probabilistic programming.Readers will find clear, rigorous explanations of Gibbs measures and their probabilistic underpinnings, practical guidance on Gibbs sampling, MCMC, and interacting particle systems, case studies ranging from deep linear networks to transformer architectures, and insights into emerging trends, including modern associative memories and thermodynamics of autoregressive language modeling.Whether you are a researcher, graduate student, or experienced practitioner, this book provides the theoretical depth and practical tools needed to harness Gibbs measures for robust, efficient, and interpretable machine learning models.
