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DESCRIPTION: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.
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