https://github.com/turinglang/slicesampling.jl
Slice sampling algorithms in Julia
https://github.com/turinglang/slicesampling.jl
Last synced: 4 months ago
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Slice sampling algorithms in Julia
- Host: GitHub
- URL: https://github.com/turinglang/slicesampling.jl
- Owner: TuringLang
- License: mit
- Created: 2023-11-30T08:17:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-23T06:10:43.000Z (about 1 year ago)
- Last Synced: 2024-05-23T06:42:31.900Z (about 1 year ago)
- Language: Julia
- Size: 132 KB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Slice Sampling Algorithms in Julia
[](https://TuringLang.org/SliceSampling.jl/stable/)
[](https://TuringLang.org/SliceSampling.jl/dev/)
[](https://github.com/Red-Portal/SliceSampling.jl/actions/workflows/CI.yml?query=branch%3Amain)
[](https://codecov.io/gh/Red-Portal/SliceSampling.jl)
This package implements slice sampling algorithms accessible through the `AbstractMCMC` [interface](https://github.com/TuringLang/AbstractMCMC.jl).
For general usage, please refer to [here](https://turinglang.org/SliceSampling.jl/dev/general/).
## Implemented Algorithms
### Univariate Slice Sampling Algorithms
- Univariate slice sampling ([Slice](https://turinglang.org/SliceSampling.jl/dev/univariate_slice/)) algorithms by R. Neal [^N2003]:
- Fixed window (`Slice`)
- stepping-out window adaptation (`SliceSteppingOut`)
- doubling-out window adaptation (`SliceDoublingOut`)
### Meta Multivariate Samplers for Augmenting Univariate Samplers
- Random permutation coordinate-wise Gibbs sampling[^GG1984] (`RandPermGibbs`)
- Hit-and-run sampling[^BRS1993] (`HitAndRun`)
### Multivariate Slice Sampling Algorithms
- Latent slice sampling ([LSS](https://turinglang.org/SliceSampling.jl/dev/latent_slice/)) by Li and Walker[^LW2023] (`LatentSlice`)
- Gibbsian polar slice sampling ([GPSS](https://turinglang.org/SliceSampling.jl/dev/gibbs_polar/)) by P. Schär, M. Habeck, and D. Rudolf[^SHR2023] (`GibbsPolarSlice`)
## Example with Turing Models
This package supports the [Turing](https://github.com/TuringLang/Turing.jl) probabilistic programming framework:
```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)
```
The following slice samplers can also be used as a conditional sampler in `Turing.Gibbs` sampler:
* For multidimensional variables:
* `RandPermGibbs`
* `HitAndRun`
* For unidimensional variables:
* `Slice`
* `SliceSteppingOut`
* `SliceDoublingOut`
See the following example:
```julia
using Distributions
using Turing
using SliceSampling
@model function simple_choice(xs)
p ~ Beta(2, 2)
z ~ Bernoulli(p)
for i in 1:length(xs)
if z == 1
xs[i] ~ Normal(0, 1)
else
xs[i] ~ Normal(2, 1)
end
end
end
sampler = Turing.Gibbs(
:p => externalsampler(SliceSteppingOut(2.0)),
:z => PG(20, :z),
)
n_samples = 1000
model = simple_choice([1.5, 2.0, 0.3])
sample(model, sampler, n_samples)
```
[^N2003]: Neal, R. M. (2003). Slice sampling. The annals of statistics, 31(3), 705-767.
[^LW2023]: Li, Y., & Walker, S. G. (2023). A latent slice sampling algorithm. Computational Statistics & Data Analysis, 179, 107652.
[^SHR2023]: Schär, P., Habeck, M., & Rudolf, D. (2023, July). Gibbsian polar slice sampling. In International Conference on Machine Learning.
[^GG1984]: Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6).
[^BRS1993]: Bélisle, C. J., Romeijn, H. E., & Smith, R. L. (1993). Hit-and-run algorithms for generating multivariate distributions. Mathematics of Operations Research, 18(2), 255-266.