A Comparison of Likelihood-Free Methods With and Without Summary Statistics

03/03/2021
by   Christopher Drovandi, et al.
0

Likelihood-free methods are useful for parameter estimation of complex models with intractable likelihood functions for which it is easy to simulate data. Such models are prevalent in many disciplines including genetics, biology, ecology and cosmology. Likelihood-free methods avoid explicit likelihood evaluation by finding parameter values of the model that generate data close to the observed data. The general consensus has been that it is most efficient to compare datasets on the basis of a low dimensional informative summary statistic, incurring information loss in favour of reduced dimensionality. More recently, researchers have explored various approaches for efficiently comparing empirical distributions in the likelihood-free context in an effort to avoid data summarisation. This article provides a review of these full data distance based approaches, and conducts the first comprehensive comparison of such methods, both qualitatively and empirically. We also conduct a substantive empirical comparison with summary statistic based likelihood-free methods. The discussion and results offer guidance to practitioners considering a likelihood-free approach. Whilst we find the best approach to be problem dependent, we also find that the full data distance based approaches are promising and warrant further development. We discuss some opportunities for future research in this space.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/15/2020

Detecting conflicting summary statistics in likelihood-free inference

Bayesian likelihood-free methods implement Bayesian inference using simu...
10/03/2018

An easy-to-use empirical likelihood ABC method

Many scientifically well-motivated statistical models in natural, engine...
09/11/2019

Efficient Bayesian synthetic likelihood with whitening transformations

Likelihood-free methods are an established approach for performing appro...
09/21/2018

Parameter inference and model comparison using theoretical predictions from noisy simulations

When inferring unknown parameters or comparing different models, data mu...
05/22/2018

Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits

Approximate Bayesian computation is an established and popular method fo...
02/16/2018

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

Inference for population genetics models is hindered by computationally ...
07/30/2018

Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review

This paper is a review of a crucial topic in data assimilation: the join...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.