# Nested sampling for Bayesian parameter estimation in Julia

**URL:** <https://discourse.julialang.org/t/nested-sampling-for-bayesian-parameter-estimation-in-julia/96105>\
**Category:** General Usage\
**Tags:** inference, bayesian-inference\
**Created:** [March 15, 2023, 4:32am UTC](https://discourse.julialang.org/t/nested-sampling-for-bayesian-parameter-estimation-in-julia/96105 "2023-03-15T04:32:33Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![tomkimpson](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tomkimpson/32/39168_2.png) [@tomkimpson](https://discourse.julialang.org/u/tomkimpson)\
**Post date:** [March 15, 2023, 4:32am UTC](https://discourse.julialang.org/t/nested-sampling-for-bayesian-parameter-estimation-in-julia/96105/1 "2023-03-15T04:32:33Z")

</div>

It is a common problem to want to infer parameters (Bayesian) given some data, a set of priors on those parameters, and a (parameterised) model.

In the past, coming from Python, I have used tools such as [Bilby](https://lscsoft.docs.ligo.org/bilby/basics-of-parameter-estimation.html), a Python library specifically for gravitational wave inference problems.

**What are the current leading options for doing the same thing in Julia?** Ideally one would be able to specify the data, a likelihood function, a null likelihood function (i.e. a function which returns the likelihood given the null model) and the priors on the parameters, and get returned a probability distribution for the parameters.

Options I have seen are:

- [GitHub - TuringLang/NestedSamplers.jl: Implementations of single and multi-ellipsoid nested sampling](https://github.com/TuringLang/NestedSamplers.jl)
- [Welcome to UltraNest’s documentation! — UltraNest 3.5.7 documentation](https://johannesbuchner.github.io/UltraNest/index.html)

Is one of these particularly recommended? Are there other standard libraries in Julia for solving these kinds of problems?

Thanks