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

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.

5 pages, 8 figures; corrections and additions to match version published in MNRAS Letters; added reference to published version

References in corpus (4)

• Massive Lossless Data Compression and Multiple Parameter Estimation from Galaxy Spectra
• Fast identification of transits from light-curves
• Fast Parameter Estimation from the CMB Power Spectrum
• Classifying LISA gravitational wave burst signals using Bayesian evidence

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