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MAINT: add colins blog posts on hmc (#40)
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‎_posts/2019-01-02-bayesian-inference-reading.md‎

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@@ -54,12 +54,19 @@ So without further ado...
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2. [A Conceptual Introduction to Hamiltonian Monte
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Carlo](https://arxiv.org/abs/1701.02434) by Michael Betancourt. An excellent
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paper for a solid conceptual understanding and principled intuition for HMC.
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3. [The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte
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3. [Exercises in Automatic Differentiation using `autograd` and
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`jax`](https://colindcarroll.com/2019/04/06/exercises-in-automatic-differentiation-using-autograd-and-jax/)
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by Colin Carroll. This is the first in a series of blog posts that explain
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HMC from the very beginning. See also [Hamiltonian Monte Carlo from
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Scratch](https://colindcarroll.com/2019/04/11/hamiltonian-monte-carlo-from-scratch/)
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and [Step Size Adaptation in Hamiltonian Monte
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Carlo](https://colindcarroll.com/2019/04/21/step-size-adaptation-in-hamiltonian-monte-carlo/).
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4. [The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte
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Carlo](https://arxiv.org/abs/1111.4246) by Matthew Hoffman and Andrew Gelman.
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The original NUTS paper.
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4. [MCMC Using Hamiltonian
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5. [MCMC Using Hamiltonian
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Dynamics](http://www.mcmchandbook.net/HandbookChapter5.pdf) by Radford Neal.
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5. [Hamiltonian Monte Carlo in
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6. [Hamiltonian Monte Carlo in
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PyMC3](https://colindcarroll.com/talk/hamiltonian-monte-carlo/) by Colin
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Carroll.
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