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A comprehensive textbook blending information theory, Bayesian inference, and machine learning. The book introduces concepts such as entropy, coding, Bayesian statistics, neural networks, kernel methods, and probabilistic modeling — unified under a coherent probabilistic framework. Widely praised for clarity and breadth, it's used by students and researchers in statistics, computer science, and related fields.

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245: $a: Information Theory, Inference, and Learning Algorithms
245: $b:
082: $a:
100: $a: David J. C. MacKay
650: $a: Machine learning
250: $a:
440: $v:
250: $b: Cambridge University Press
250: $c: 2003
020: $a: 978-0-521-64298-9
300: $a: 640