Use this URL to cite or link to this record in EThOS: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547890 |
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Title: | Differential geometric MCMC methods and applications | ||||||
Author: | Calderhead, Ben |
ISNI:
0000 0004 2712 760X
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Awarding Body: | University of Glasgow | ||||||
Current Institution: | University of Glasgow | ||||||
Date of Award: | 2011 | ||||||
Availability of Full Text: |
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Abstract: | |||||||
This thesis presents novel Markov chain Monte Carlo methodology that exploits the natural representation of a statistical model as a Riemannian manifold. The methods developed provide generalisations of the Metropolis-adjusted Langevin algorithm and the Hybrid Monte Carlo algorithm for Bayesian statistical inference, and resolve many shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlation structure. The performance of these Riemannian manifold Markov chain Monte Carlo algorithms is rigorously assessed by performing Bayesian inference on logistic regression models, log-Gaussian Cox point process models, stochastic volatility models, and both parameter and model level inference of dynamical systems described by nonlinear differential equations.
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Supervisor: | Not available | Sponsor: | Not available | ||||
Qualification Name: | Thesis (Ph.D.) | Qualification Level: | Doctoral | ||||
EThOS ID: | uk.bl.ethos.547890 | DOI: | Not available | ||||
Keywords: | QC Physics ; QA Mathematics ; Q Science (General) | ||||||
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