# MCMC landscape

**URL:** <https://discourse.julialang.org/t/mcmc-landscape/25654>\
**Category:** Statistics\
**Tags:** question\
**Created:** [June 25, 2019, 3:23pm UTC](https://discourse.julialang.org/t/mcmc-landscape/25654 "2019-06-25T15:23:28Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [June 26, 2019, 12:22am UTC](https://discourse.julialang.org/t/mcmc-landscape/25654/11 "2019-06-26T00:22:41Z")

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DiffEqBayes.jl is a library that lets people use Bayesian inference on ODEs without knowledge of those libraries.

[https://github.com/JuliaDiffEq/DiffEqBayes.jl](https://github.com/JuliaDiffEq/DiffEqBayes.jl)

What we’ve found so far is that Turing’s NUTS is the most stable, even more stable than the auto-generated Stan kernels. DynamicHMC diverges for us sometimes. Turing is much faster than it used to be. These tests show that it’s the thing to use, at least for now.

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