- 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
- Convariance property: https://en.wikipedia.org/wiki/Covariance_matrix#Properties
- 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
- Great interactive book: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
- 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
- Good introduction on GP http://mlss2011.comp.nus.edu.sg/uploads/Site/lect1gp.pdf
- 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
- Hybrid Monte-Carlo Sampling http://deeplearning.net/tutorial/hmc.html
- Bishop: PRML
- Elementary Statistics Learn: https://web.stanford.edu/~hastie/ElemStatLearn/