Work

Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time

Year: 2020

Type: conference-paper

Abstract: We present algorithms (a) for nested neural likelihood-to-evidence ratio estimation, and (b) for simulation reuse via an inhomogeneous Poisson point process cache of parameters and corresponding simulations. Together, these algorithms enable automatic and extremely simulator efficient estimation of marginal and joint posteriors. The algorithms are applicable to a wide range of physics and astronomy problems and typically offer an order of magnitude better simulator efficiency than traditional likelihood-based sampling methods. Our approach is an example of likelihood-free inference, thus it is also applicable to simulators which do not offer a tractable likelihood function. Simulator runs are never rejected and can be automatically reused in future analysis. As functional prototype implementation we provide the open-source software package swyft.

Source: Open Repository and Bibliography (University of Liège)

DOI: 10.48550/arxiv.2011.13951

Authors: Miller, Benjamin Kurt, Cole, Alex, Louppe, Gilles, Weniger, Christoph

Institutions: Institute for Theoretical Physics Amsterdam, University of Amsterdam, University of Liège

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Universiteit van Amsterdam

Awards: G2975633183, G434337789, G5904312298