Bayesian Models.

This project consists of highly optimized Python implementations of three fundamental Bayesian statistical models. Each implementation focuses on computational efficiency, numerical stability, and vectorization, replacing standard iterative loops with linear algebra operations (NumPy/SciPy) and JIT compilation (Numba) where appropriate.

Content

GP.py Model: Gaussian Process Regression Key Techniques: Cholesky Decomposition, SciPy cdist

BayesianFM.py Model: 3D Bayesian Finite Mixture Key Techniques: Vectorized Gibbs Sampling, Log-Sum-Exp Trick

BayesianHMMGibbs.py Model: Bayesian Hidden Markov Model Key Techniques: FFBS Algorithm, Numba JIT Compilation