# Differential evolution MCMC in Julia?

**URL:** <https://discourse.julialang.org/t/differential-evolution-mcmc-in-julia/104713>\
**Category:** Probabilistic Programming\
**Tags:** question, turing, mcmc\
**Created:** [October 7, 2023, 8:25pm UTC](https://discourse.julialang.org/t/differential-evolution-mcmc-in-julia/104713 "2023-10-07T20:25:06Z")\
**Posts on this page:** 1\
**Showing post:** 21

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**Author:** ![Red-Portal](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/red-portal/32/9102_2.png) [@Red-Portal](https://discourse.julialang.org/u/Red-Portal)\
**Post date:** [May 26, 2024, 10:32pm UTC](https://discourse.julialang.org/t/differential-evolution-mcmc-in-julia/104713/21 "2024-05-26T22:32:47Z")

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I am happy to announce that `SliceSampling` now officially supports Turing! There are also plan to make it officially part of Turing in the near future.

> [@\[ANN\] SliceSampling.jl - Efficient zeroth-order MCMC algorithms](https://discourse.julialang.org/t/ann-slicesampling-jl-efficient-zeroth-order-mcmc-algorithms/114756):
>
> Hi all, As part of the Turing ecosystem, we recently created a collection of slice-sampling MCMC algorithms. Why slice sampling algorithms are great: For low dimensional problems, they perform very well with minimal tuning Even if the tuning is off, they automatically adjust the amount of computation and result in adequate samples. They handle complex posterior geometry, such as multi modality, very well. This is because they don’t rely on gradient information. We provide the following algo…

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