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CONTRIBUTIONS TO COMPUTATIONAL BAYESIAN STATISTICS

Part of: Probabilistic methods, simulation and stochastic differential equations

Published online by Cambridge University Press:  05 February 2020

LEAH SOUTH Open the ORCID record for LEAH SOUTH [Opens in a new window]
LEAH SOUTH*
Affiliation:
School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane City, Queensland4000, Australia email [email protected]
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Abstract

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Keywords

Bayesian statisticsapproximate Bayesian computationsequential Monte Carlonested samplingmarginal likelihoodcontrol variatescell biologyordinary differential equations

MSC classification

Primary: 65C05: Monte Carlo methods
Secondary: 65C60: Computational problems in statistics
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 101 , Issue 2 , April 2020 , pp. 348 - 349
Copyright
© 2020 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to Queensland University of Technology in January 2019; degree approved on 28 August 2019; principal supervisor Christopher Drovandi, associate supervisor Anthony Pettit.

References

Price (née South), L. F., Drovandi, C. C., Lee, A. and Nott, D. J., ‘Bayesian synthetic likelihood’, J. Comput. Graph. Statist. 27(1) (2018), 11 pages.Google Scholar
Salomone, R., South, L. F., Drovandi, C. C. and Kroese, D. P., ‘Unbiased and consistent nested sampling via sequential Monte Carlo’, Preprint, 2018, arXiv:1805.03924.Google Scholar
South, L. F., Oates, C. J., Mira, A. and Drovandi, C. C., ‘Regularised zero-variance control variates for high-dimensional variance reduction’, Preprint, 2018, arXiv:1811.05073.Google Scholar
South, L. F., Pettitt, A. N. and Drovandi, C. C., ‘Sequential Monte Carlo samplers with independent MCMC proposals’, Bayesian Anal. 14(3) (2019), 753–776.CrossRefGoogle Scholar