# Modern Computational Methods for Bayesian Inference — A Reading List

**URL:** <https://discourse.pymc.io/t/modern-computational-methods-for-bayesian-inference-a-reading-list/2478>\
**Category:** Sharing\
**Created:** [January 5, 2019, 12:36am UTC](https://discourse.pymc.io/t/modern-computational-methods-for-bayesian-inference-a-reading-list/2478 "2019-01-05T00:36:58Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![\_eigenfoo](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/_eigenfoo/32/3059_2.png) [@\_eigenfoo](https://discourse.pymc.io/u/_eigenfoo)\
**Post date:** [January 5, 2019, 12:36am UTC](https://discourse.pymc.io/t/modern-computational-methods-for-bayesian-inference-a-reading-list/2478/1 "2019-01-05T00:36:58Z")

</div>

Hello!

I compiled a list of read materials to understand how the PyMC3 backends actually work. I focused especially on HMC and ADVI - the two methods that I always said I used but never really understood! 😄

I thought that it might be worth sharing with the discourse community. Hope you like it! If you have any comments/suggestions, [drop me a line](https://twitter.com/@_eigenfoo)!

> **[Modern Computational Methods for Bayesian Inference — A Reading List](https://eigenfoo.xyz/bayesian-inference-reading/)**
>
> An annotated reading list on modern computational methods for Bayesian inference — Markov chain Monte Carlo (MCMC), variational inference (VI) and some other (more experimental) methods.