# \[ANN\] SliceSampling.jl - Efficient zeroth-order MCMC algorithms

**URL:** <https://discourse.julialang.org/t/ann-slicesampling-jl-efficient-zeroth-order-mcmc-algorithms/114756>\
**Category:** Package Announcements\
**Tags:** announcement, turing, bayesian-inference, mcmc\
**Created:** [May 26, 2024, 10:20pm UTC](https://discourse.julialang.org/t/ann-slicesampling-jl-efficient-zeroth-order-mcmc-algorithms/114756 "2024-05-26T22:20:23Z")\
**Posts on this page:** 3\
**Page:** 1

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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, 10:20pm UTC](https://discourse.julialang.org/t/ann-slicesampling-jl-efficient-zeroth-order-mcmc-algorithms/114756/1 "2024-05-26T22:20:23Z")

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Hi all,

As part of the Turing ecosystem, we recently created a collection of slice-sampling MCMC algorithms:

> **[GitHub - TuringLang/SliceSampling.jl: Slice sampling algorithms in Julia](https://github.com/TuringLang/SliceSampling.jl)**
>
> Slice sampling algorithms in Julia. Contribute to TuringLang/SliceSampling.jl development by creating an account on GitHub.

Why slice sampling algorithms are great:

- Don’t need gradients!
- 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 algorithms:

- The classic univariate slice sampling algorithms \[1\]
- Latent slice sampling\[2\]
- Gibbsian polar slice sampling\[3\]

The package supports Turing and can be used as follows:

```julia
using Distributions
using Turing
using SliceSampling

@model function demo()
    s ~ InverseGamma(3, 3)
    m ~ Normal(0, sqrt(s))
end

sampler = RandPermGibbs(SliceSteppingOut(2))
n_samples = 10000
model = demo()
sample(model, externalsampler(sampler), n_samples; initial_params=[exp(1.0), 0.0])

```

There are also plans to make it officially part of Turing so that it can be used in combination with `Turing.Gibbs`, so stay tuned!

* * *

1. Neal, R. M. (2003). Slice sampling. The annals of statistics, 31(3), 705-767. 

2. Li, Y., & Walker, S. G. (2023). A latent slice sampling algorithm. Computational Statistics & Data Analysis, 179, 107652. 

3. Schär, P., Habeck, M., & Rudolf, D. (2023, July). Gibbsian polar slice sampling. In International Conference on Machine Learning.

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**Author:** ![jling](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jling/32/212909_2.png) [@jling](https://discourse.julialang.org/u/jling)\
**Post date:** [May 27, 2024, 2:42am UTC](https://discourse.julialang.org/t/ann-slicesampling-jl-efficient-zeroth-order-mcmc-algorithms/114756/2 "2024-05-27T02:42:12Z")

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Great work! I’m wondering does this tackle some of the problems that need reversible jump mcmc (i.e. transdimensional sampling)

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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 27, 2024, 2:53am UTC](https://discourse.julialang.org/t/ann-slicesampling-jl-efficient-zeroth-order-mcmc-algorithms/114756/3 "2024-05-27T02:53:28Z")

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Hi! Unfortunately, no. For RJMCMC problems, you need to use an RJMCMC sampler. However, I personally have been using slice sampling within RJMCMC samplers quite successfully.