Inference in Complex Systems Using Multi-Phase MCMC Sampling With Gradient Matching Burn-in (original) (raw)

Lazarus, Alan, Husmeier, Dirk ORCID logoORCID: https://orcid.org/0000-0003-1673-7413 and Papamarkou, Theodore ORCID logoORCID: https://orcid.org/0000-0002-9689-543X(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: Papamarkou, Dr Theodore and Lazarus, Mr Alan and Husmeier, Professor Dirk
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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Funder and Project Information

1

Computational inference in systems biology

Dirk Husmeier

EP/L020319/1

M&S - STATISTICS

Deposit and Record Details

ID Code: 144287
Depositing User: Professor Dirk Husmeier
Datestamp: 18 Jul 2017 08:19
Last Modified: 02 May 2025 14:41
Date of first online publication: 3 July 2017
Date Deposited: 18 July 2017
Data Availability Statement: No