Inference in Complex Systems Using Multi-Phase MCMC Sampling With Gradient Matching Burn-in

Lazarus, A., Husmeier, D. and Papamarkou, T. (2017) Inference in Complex Systems Using Multi-Phase MCMC Sampling With Gradient Matching Burn-in. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 52-57.

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Abstract

We propose a novel method for parameter inference that builds on the current research in gradient matching surrogate likelihood spaces. Adopting a three phase technique, we demonstrate that it is possible to obtain parameter estimates of limited bias whilst still adopting the paradigm of the computationally cheap surrogate approximation.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lazarus, Alan and Husmeier, Professor Dirk and Papamarkou, Dr Theodore
Authors: Lazarus, A., Husmeier, D., and Papamarkou, T.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Proceedings of the 32nd International Workshop on Statistical Modelling: 52-57
Publisher Policy:Reproduced with the permission of the Editor

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
633291Computational inference in systems biologyDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/L020319/1M&S - STATISTICS