Work

pyLAIS: A Python package for Layered Adaptive Importance Sampling

Year: 2024

Type: software-paper

Abstract: Monte Carlo (MC) techniques are widely used in the calculation of complex integrals and approximation of distributions. The most widely used families of MC methods are Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS). There are many separate implementations and packages, available online regarding MCMC or IS methods.Moreover, both techniques present different drawbacks and advantages.In this paper, we introduce a flexible Python implementation of the layered adaptive importance sampling (LAIS) algorithm. LAIS combines the benefits of MCMC and IS schemes: the exploration of the state space by Markov chains and the low variance estimations provides by advanced IS methods. More precisely, LAIS allows the sampling of complex distributions (and/or approximation of integrals involving complex distributions) through the combined use of (possibly sophisticated) MCMC schemes and multiple importance sampling techniques. In addition, the modular nature of the algorithm itself allows for programming in separate methods which provides greater flexibility in choosing the desired MCMC technique.

Source: SoftwareX

DOI: 10.1016/j.softx.2024.101976

Authors: Ernesto Curbelo, Luca Martino, David Delgado-Gómez

Institutions: Universidad Carlos III de Madrid, University of Catania

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $970

Funders: Università di Catania, Agencia Estatal de Investigación, Office of Naval Research Global