# Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

@article{Cole2021FastAC, title={Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation}, author={Alex Cole and Benjamin Kurt Miller and Samuel J. Witte and Maxwell Xu Cai and M. W. Grootes and Francesco Nattino and C Weniger}, journal={ArXiv}, year={2021}, volume={abs/2111.08030} }

Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods. In this paper we describe how Truncated Marginal Neural Ratio Estimation (tmnre) (a new approach in so-called simulation-based inference) naturally evades these issues, improving the (i) efficiency, (ii) scalability, and (iii) trustworthiness of the inference. Using measurements of the Cosmic…

## Figures from this paper

## References

SHOWING 1-10 OF 86 REFERENCES

Fast likelihood-free cosmology with neural density estimators and active learning

- PhysicsMonthly Notices of the Royal Astronomical Society
- 2019

Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational effects that can be…

Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

- Physics
- 2018

Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models…

Truncated Marginal Neural Ratio Estimation

- Mathematics, PhysicsArXiv
- 2021

This work presents a neural simulation-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability, which is unique among modern algorithms.

Lossless, scalable implicit likelihood inference for cosmological fields

- PhysicsJournal of Cosmology and Astroparticle Physics
- 2021

We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two cases where the…

Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

- Computer Science, MathematicsNIPS
- 2016

This work proposes a new approach to likelihood-free inference based on Bayesian conditional density estimation, which requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.

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

- Computer Science, PhysicsArXiv
- 2020

This work presents algorithms for nested neural likelihood-to-evidence ratio estimation and simulation reuse via an inhomogeneous Poisson point process cache of parameters and corresponding simulations that enable automatic and extremely simulator efficient estimation of marginal and joint posteriors.

MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

- Physics
- 2009

We present further development and the first public release o f our multimodal nested sampling algorithm, called MULTINEST. This Bayesian inference tool calculates the evidence, with an associated…

Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference

- Computer Science, PhysicsArXiv
- 2021

This work presents a novel method that enables amortized inference over arbitrary subsets of the parameters, without resorting to numerical integration, which makes interpretation of the posterior more convenient.

Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks

- Computer Science, MathematicsArXiv
- 2020

This work proposes direct estimation of lower-dimensional marginal distributions, bypassing high-dimensional density estimation or high- dimensional Markov chain Monte Carlo sampling, and constructs a simple hierarchy of fast neural regression models, called Moment Networks, that compute increasing moments of any desired lower- dimensional marginal posterior density.

Nested sampling for general Bayesian computation

- Mathematics
- 2006

Nested sampling estimates directly how the likelihood function relates to prior mass. The evidence (alternatively the marginal likelihood, marginal den- sity of the data, or the prior predictive) is…