jaxns 2.3.4

Nested Sampling in JAX

Main Status:

Develop Status:

Mission: To make nested sampling faster, easier, and more powerful

What is it?

JAXNS is:

1. a probabilistic programming framework using nested sampling as the engine;
2. coded in JAX in a manner that allows lowering the entire inference algorithm to XLA primitives, which are JIT-compiled for high performance;
3. continuously improving on its mission of making nested sampling faster, easier, and more powerful; and
4. citable, and you can read an (old) pre-print here: (https://arxiv.org/abs/2012.15286).

Documentation

You can read the documentation here.

Install

Notes:

1. JAXNS requires >= Python 3.8.
2. It is always highly recommended to use a unique virtual environment for each project. To use miniconda, have it installed, and run

# To create a new env, if necessary
conda create -n jaxns_py python=3.11
conda activate jaxns_py

For end users

Install directly from PyPi,

pip install jaxns

For development

Clone repo git clone https://www.github.com/JoshuaAlbert/jaxns.git, and install:

cd jaxns
pip install -r requirements.txt
pip install -r requirements-tests.txt
pip install -r requirements-examples.txt
pip install .

Getting help and contributing examples

Do you have a neat Bayesian problem, and want to solve it with JAXNS? I'm really encourage anyone in either the scientific community or industry to get involved and join the discussion forum. Please use the github discussion forum for getting help, or contributing examples/neat use cases.

Quick start

Checkout the examples here.

Caveats

The caveat is that you need to be able to define your likelihood function with JAX. This is usually no big deal because JAX is just a replacement for NumPy and many likelihoods can be expressed such. If you're unfamiliar, take a quick tour of JAX (https://jax.readthedocs.io/en/latest/notebooks/quickstart.html).

Speed test comparison with other nested sampling packages

JAXNS is really fast because it uses JAX. JAXNS is much faster than PolyChord, MultiNEST, and dynesty, typically achieving two to three orders of magnitude improvement in run time, for models with cheap likelihood evaluations. This is shown in (https://arxiv.org/abs/2012.15286).

Recently JAXNS has implemented Phantom-Powered Nested Sampling, which significantly reduces the number of required likelihood evaluations. This is shown in (https://arxiv.org/abs/).

Note on performance with parallelisation

Note, that this is an experimental feature.

If you set num_parallel_workers > 1 you will use jax.pmap under the hood for parallelisation. This is a very powerful feature, but it is important to understand how it works. It runs identical copies of the nested sampling algorithm on multiple devices. There is a two-part stopping condition. First, each copy goes until the user defined stopping condition is met per device. Then, it performs an all-gather and finds at the highest likelihood contour among all copies, and continues all copies hit this likelihood contour. This ensures consistency of depth across all copies. We then merge the copies and compute the final results.

The algorithm is fairly memory bound, so running parallelisation over multiple CPUs on the same machine may not yield the expected speed up, and depends how expensive the likelihood evaluations are. Running over separate physical devices is the best way to achieve speed up.

Change Log

21 Dec, 2023 -- JAXNS 2.3.4 released. Correction for ESS and logZ uncert. parameter_estimation mode.

20 Dec, 2023 -- JAXNS 2.3.2/3 released. Improved default parameters. difficult_model mode. Improve plotting.

18 Dec, 2023 -- JAXNS 2.3.1 released. Paper open science release. Default parameters from paper.

11 Dec, 2023 -- JAXNS 2.3.0 released. Released of Phantom-Powered Nested Sampling algorithm.

5 Oct, 2023 -- JAXNS 2.2.6 released. Minor update to evidence maximisation.

3 Oct, 2023 -- JAXNS 2.2.5 released. Parametrised priors, and evidence maximisation added.

24 Sept, 2023 -- JAXNS 2.2.4 released. Add marginalising from saved U samples.

28 July, 2023 -- JAXNS 2.2.3 released. Bug fix for singular priors.

26 June, 2023 -- JAXNS 2.2.1 released. Multi-ellipsoidal sampler added back in. Adaptive refinement disabled, as a bias has been detected in it.

15 June, 2023 -- JAXNS 2.2.0 released. Added support to allow TFP bijectors to defined transformed distributions. Other minor improvements.

15 April, 2023 -- JAXNS 2.1.0 released. pmap used on outer-most loops allowing efficient device-device communication during parallel runs.

8 March, 2023 -- JAXNS 2.0.1 released. Changed how we're doing annotations to support python 3.8 again.

3 January, 2023 -- JAXNS 2.0 released. Complete overhaul of components. New way to build models.

5 August, 2022 -- JAXNS 1.1.1 released. Pytree shaped priors.

2 June, 2022 -- JAXNS 1.1.0 released. Dynamic sampling takes advantage of adaptive refinement. Parallelisation. Bayesian opt and global opt modules.

30 May, 2022 -- JAXNS 1.0.1 released. Improvements to speed, parallelisation, and structure of code.

9 April, 2022 -- JAXNS 1.0.0 released. Parallel sampling, dynamic search, and adaptive refinement. Global optimiser released.

2 Jun, 2021 -- JAXNS 0.0.7 released.

13 May, 2021 -- JAXNS 0.0.6 released.

8 Mar, 2021 -- JAXNS 0.0.5 released.

8 Mar, 2021 -- JAXNS 0.0.4 released.

7 Mar, 2021 -- JAXNS 0.0.3 released.

28 Feb, 2021 -- JAXNS 0.0.2 released.

28 Feb, 2021 -- JAXNS 0.0.1 released.

1 January, 2021 -- Paper submitted

Star History