- A good introduction on MCMC (chinese version) https://www.cnblogs.com/xbinworld/p/4266146.html
- Steps on using gibbs sampling: http://www.mit.edu/~ilkery/papers/GibbsSampling.pdf
- Bayesian Gibbs: https://kieranrcampbell.github.io/blog/2016/05/15/gibbs-sampling-bayesian-linear-regression.html
- statistics computation: https://github.com/cliburn/sta-663-2016/tree/master/lectures
- Tianqi chen HMC : https://arxiv.org/pdf/1402.4102.pdf
- A Complete Recipe for Stochastic Gradient MCMC: https://arxiv.org/pdf/1506.04696.pdf
- Bayesian Learning via Stochastic Gradient Langevin Dynamics: https://www.ics.uci.edu/~welling/publications/papers/stoclangevin_v6.pdf
- Hamiltonian monte carlo https://arxiv.org/pdf/1701.02434.pdf
- Hamiltonian MC video: https://www.youtube.com/watch?v=VnNdhsm0rJQ
- Hamiltonian MC: https://www.youtube.com/watch?v=pHsuIaPbNbY
- Neal, R.M. Bayesian learning via stochastic dynamics. In NIPS 1993. https://pdfs.semanticscholar.org/d275/cf94e620bf5b3776bba8a88acccdcfcd9a19.pdf
- Neal, R.M. Bayesian learning for neural networks. PhD thesis https://www.cs.toronto.edu/~radford/ftp/thesis.pdf
- Ghahramani, Z. (2015) https://www.repository.cam.ac.uk/bitstream/handle/1810/248538/Ghahramani%202015%20Nature.pdf