Bayesian introduction

• Guassian: http://cs229.stanford.edu/section/more_on_gaussians.pdf
• http://www.scholarpedia.org/article/Bayesian
• Check data normal: http://allendowney.blogspot.com/2013/08/are-my-data-normal.html

Guassina conditional Mean and Variance

• https://stats.stackexchange.com/questions/30588/deriving-the-conditional-distributions-of-a-multivariate-normal-distribution

Covariance Property

• Convariance property: https://en.wikipedia.org/wiki/Covariance_matrix#Properties

Bayesian Linear Model

• Bayesian linear probit model implementation http://www.herbrich.me/papers/adpredictor.pdf
• Trueskll: https://papers.nips.cc/paper/3079-trueskilltm-a-bayesian-skill-rating-system.pdf
• https://tgmstat.wordpress.com/2013/08/07/bayesian-linear-regression-model-simple-yet-useful-results/
• https://en.wikipedia.org/wiki/Bayesian_linear_regression

Kalman Filter

• Great interactive book: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

BayesianML

• Prime on bayesian inference http://personal.vu.nl/a.f.de.vos/primer/primer.pdf
• Good overview at NIPS 2016: http://bayesiandeeplearning.org/2016/slides/nips16bayesdeep.pdf
• Deep Neural Networks as Gaussian Processes https://arxiv.org/pdf/1704.04289.pdf

Guassian Process

• Good introduction on GP http://mlss2011.comp.nus.edu.sg/uploads/Site/lect1gp.pdf

Blogs

• http://fastml.com/bayesian-machine-learning/
• Check data normal: http://allendowney.blogspot.com/2013/08/are-my-data-normal.html
• Allen Downey blog: http://allendowney.blogspot.com/2018/
• http://twiecki.github.io/blog/2017/03/14/random-walk-deep-net/
• One of Edward lead: http://dustintran.com/#publications
• hybrid Monte Carlo https://mqshen.gitbooks.io/prml/Chapter11/monte/hybrid_monte_carlo.html

Tutorials

• Hybrid Monte-Carlo Sampling http://deeplearning.net/tutorial/hmc.html

Books

• Bishop: PRML
• Elementary Statistics Learn: https://web.stanford.edu/~hastie/ElemStatLearn/

Software

• Stan: https://mc-stan.org/
• Pymc3: https://github.com/pymc-devs/pymc3