gerdm/prml

Implements core algorithms across 14 chapters—from Bayesian inference and linear models to graphical models, sampling methods, and dimensionality reduction—with reproducible Jupyter notebooks that reconstruct the book's figures and mathematical derivations. Uses NumPy for numerical computation and visualization libraries to replicate PRML's canonical diagrams, enabling interactive exploration of concepts like variational inference, Gaussian processes, and hidden Markov models alongside exercise solutions.

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