# Eryn : A multi-purpose sampler for Bayesian inference

arXiv: https://arxiv.org/abs/2303.02164 · HTML: https://mlsanity.com/paper/2303.02164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 143 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Stochastic Normalizing Flows](https://mlsanity.com/paper/2002.06707/) (2020)
- [Hamiltonian Monte Carlo for Hierarchical Models](https://mlsanity.com/paper/1312.0906/) (2013)
- [A Review of Multiple Try MCMC algorithms for Signal Processing](https://mlsanity.com/paper/1801.09065/) (2018)
- [Bayesian Methods in Cosmology](https://mlsanity.com/paper/1701.01467/) (2017)

## Abstract (arXiv)

In recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary. Data analysis in gravitational-wave astronomy is a prime example of such a case. Bayesian inference has been very successful because this technique provides a representation of the parameters as a posterior probability distribution, with uncertainties informed by the precision of the experimental measurements. During the last couple of decades, many specific advances have been proposed and employed in order to solve a large variety of different problems. In this work, we present a Markov Chain Monte Carlo (MCMC) algorithm that integrates many of those concepts into a single MCMC package. For this purpose, we have built tt Eryn, a user-friendly and multipurpose toolbox for Bayesian inference, which can be utilized for solving parameter estimation and model selection problems, ranging from simple inference questions, to those with large-scale model variation requiring trans-dimensional MCMC methods, like the LISA global fit problem. In this paper, we describe this sampler package and illustrate its capabilities on a variety of use cases.