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, 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
- Welcome to UltraNestâ€™s documentation! â€” UltraNest 3.5.7 documentation

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

Thanks