An investigation into the Multiple Optimised Parameter Estimation and Data compression algorithm

TL;DR

This paper evaluates the MOPED data compression algorithm's effectiveness in likelihood estimation, identifying conditions where it accurately preserves the likelihood and scenarios where it fails, to optimize computational efficiency.

Contribution

It provides criteria to determine when MOPED accurately represents the likelihood, enhancing its practical application for faster data analysis.

Findings

MOPED maintains the Fisher matrix at a chosen point.

MOPED can fail with multimodal or degenerate distributions.

Criteria are proposed for when MOPED is reliable.

Abstract

We investigate the use of the Multiple Optimised Parameter Estimation and Data compression algorithm (MOPED) for data compression and faster evaluation of likelihood functions. Since MOPED only guarantees maintaining the Fisher matrix of the likelihood at a chosen point, multimodal and some degenerate distributions will present a problem. We present examples of scenarios in which MOPED does faithfully represent the true likelihood but also cases in which it does not. Through these examples, we aim to define a set of criteria for which MOPED will accurately represent the likelihood and hence may be used to obtain a significant reduction in the time needed to calculate it. These criteria may involve the evaluation of the full likelihood function for comparison.