This project is to document my progress in going through Christopher Bishop's PRML book. It contains notebooks to better understand the ideas presented, as well as links to helpful papers and self-made notes.
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├── README.md
├── chapter01
│ ├── ch1_ex_tests.ipynb
│ ├── chapter1.ipynb
│ └── einsum.ipynb
├── chapter02
│ ├── Exercises.ipynb
│ ├── bayes-binomial.ipynb
│ ├── bayes-normal.ipynb
│ ├── density-estimation.ipynb
│ ├── exponential-family.ipynb
│ ├── mixtures-of-gaussians.ipynb
│ ├── periodic-variables.ipynb
│ ├── robbins-monro.ipynb
│ └── students-t-distribution.ipynb
├── chapter03
│ ├── Bayesian-linear-regression.ipynb
│ ├── equivalent-kernel.ipynb
│ ├── evidence-approximation.ipynb
│ ├── linear-models-for-regression.ipynb
│ ├── predictive-distribution.ipynb
│ └── sequential-bayesian-learning.ipynb
├── chapter04
│ ├── exercises.ipynb
│ ├── fisher-linear-discriminant.ipynb
│ ├── least-squares-classification.ipynb
│ ├── logistic-regression.ipynb
│ └── perceptron.ipynb
├── chapter05
│ ├── Ellipses.ipynb
│ ├── backpropagation.ipynb
│ ├── bayesian-neural-networks.ipynb
│ ├── imgs
│ │ └── f51.png
│ ├── mixture-density-networks.ipynb
│ ├── soft-weight-sharing.ipynb
│ └── weight-space-symmetry.ipynb
├── chapter06
│ ├── gaussian-processes.ipynb
│ └── kernel-regression.ipynb
├── chapter07
│ └── RVMs.ipynb
├── chapter08
│ ├── Trees.ipynb
│ ├── exercises.ipynb
│ ├── graphical-model-inference.ipynb
│ ├── img.jpeg
│ ├── markov-random-fields.ipynb
│ └── sum-product.ipynb
├── chapter09
│ ├── gaussian-mixture-models.ipynb
│ ├── k-means.ipynb
│ └── mixture-of-bernoulli.ipynb
├── chapter10
│ ├── exponential-mixture-gaussians.ipynb
│ ├── local-variational-methods.ipynb
│ ├── mixture-gaussians.ipynb
│ ├── variational-logistic-regression.ipynb
│ └── variational-univariate-gaussian.ipynb
├── chapter11
│ ├── adaptive-rejection-sampling.ipynb
│ ├── gibbs-sampling.ipynb
│ ├── hybrid-montecarlo.ipynb
│ ├── markov-chain-motecarlo.ipynb
│ ├── rejection-sampling.ipynb
│ ├── slice-sampling.ipynb
│ └── transformation-random-variables.ipynb
├── chapter12
│ ├── bayesian-pca.ipynb
│ ├── kernel-pca.ipynb
│ ├── principal-component-analysis.ipynb
│ └── probabilistic-pca.ipynb
└── chapter13
└── hidden-markov-model.ipynb
15 directories, 62 files