Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output

TL;DR

This paper introduces a likelihood-free inference method for simulator-based models with categorical outputs, leveraging Jensen-Shannon divergence to enable fast estimation, hypothesis testing, and confidence interval construction.

Contribution

It develops a novel asymptotic approach using Jensen-Shannon divergence for likelihood-free inference, especially suited for big data applications with categorical outputs.

Findings

Provides theoretical foundations for inference using Jensen-Shannon divergence.

Demonstrates rapid estimation and testing methods for simulator-based models.

Offers an efficient alternative to computationally intensive likelihood approximation.

Abstract

Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities. The primary focus of these research fields has been to approximate the posterior distribution of model parameters, either by various types of Monte Carlo sampling algorithms or deep neural network -based surrogate models. Frequentist inference for simulator-based models has been given much less attention to date, despite that it would be particularly amenable to applications with big data where implicit asymptotic approximation of the likelihood is expected to be accurate and can leverage computationally efficient strategies. Here we derive a set of theoretical results to enable estimation, hypothesis testing and construction of confidence intervals for model parameters using asymptotic properties of the Jensen--Shannon divergence. Such asymptotic approximation offers a rapid alternative to more computation-intensive approaches and can be attractive for diverse applications of simulator-based models. 61