# 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:** 94

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**Author:** ![mlanghinrichs](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mlanghinrichs/32/50371_2.png) [@mlanghinrichs](https://discourse.julialang.org/u/mlanghinrichs)\
**Post date:** [February 27, 2020, 6:01pm UTC](https://discourse.julialang.org/t/mcmc-landscape/25654/94 "2020-02-27T18:01:56Z")

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> [@Tamas\_Papp](#):
>
> Yeah, I recall the many quantitative biology papers I read (as an economist) when learning about this methodology 😉

All the same it seems, just different data 😃  
Thanks for the link, I’ll try to process the stuff and see how far I come!

EDIT: There is actually a topic related to my problem ([Differentiating through a Jump Problem](https://discourse.julialang.org/t/differentiating-through-a-jump-problem/31496)).

EDIT2: Just for completeness, my current conclusions:

1. Following the linked topic above, Gillespie simulations with discrete states and discrete random selection of reactions do _not_ seem to be (automatic) differentiable. You could _make_ them continuous as a SDE for AD to work (see linked topic for more info), but that’s not the path I want to take. So I’m using samplers now without the need of a gradient; i.e. [AdvancedMH](https://github.com/TuringLang/AdvancedMH.jl) as mentioned by @cpfiffer.  
If there are more non-gradient samplers in Julia, I would be glad to know! On a first glance, the currently implemented MH sampler generally works, but can have low number of effective samples.
2. Approximate Bayesian (ABC) methods should be applicable for these problems; they do not need the evaluation of a potentially costly likelihood, but are – as the name suggests – only an approximation of the posterior (how good, has to be checked case-specific). I will try them out once I run into runtime limitations. Potential Julia implementations: [ApproxBayes](https://github.com/marcjwilliams1/ApproxBayes.jl), [GpABC](https://github.com/tanhevg/GpABC.jl), [ApproximateBayesianComputing](https://github.com/eford/ApproximateBayesianComputing.jl), [ABC](https://github.com/eford/ABC.jl/blob/master/docs/src/index.md) (and maybe more).

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