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Efficient methods for fitting nonlinear non-Gaussian state-space models of wildlife population dynamics

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  • Full or part time
    Prof Len Thomas
    Prof S Buckland
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Note that this project may be co-supervised by Dr. Panagiotis Besbeas, University of Kent.

Recent years have seen an enormous growth in interest in and methods for fitting mechanistic models of wildlife population dynamics to survey data on animal numbers, survival and birth rates. Various statistical methods have been proposed, based both on maximum likelihood and Bayesian approaches. Examples include the Kalman filter (and extensions) for maximum likelihood estimation and Markov chain Monte Carlo (MCMC) or particle filtering for Bayesian models. Each has advantages and disadvantages – for example the Kalman filter is designed for linear Gaussian models (but seems to do remarkably well in other circumstances); MCMC is an excellent omnibus method but it can be difficult to derive efficient samplers (i.e. those that produce reliable answers in a reasonable amount of computer time); particle filtering is easy to program but very inefficient for some models (e.g. those with random effects). In this project, we aim to blend aspects of these approaches to increase the efficiency of the estimation. For example, we will investigate using Kalman filter estimates as importance sampling starts in a particle filter algorithm, and using the particle filter to provide proposals in an MCMC algorithm.

Potential applicants are encouraged to contact the Postgraduate Officer responsible for PhDs in Statistics, in advance of making a formal application. He is: Len Thomas, email [email protected]

To make a formal application, complete the appropriate online form at http://www.st-andrews.ac.uk/admissions/pg/apply/research/ (click on “Apply Now” on that page). You also need to provide the following supporting documentation: CV, evidence of qualifications and evidence of English language (if applicable). You are welcome to include a covering letter. You don’t need to provide a research proposal or a sample of academic written work. You will need to ask two referees to provide academic references for you – once you fill in their name on the form, they will be sent emails asking them to upload their references. Please note that we give serious consideration to both the stature of your referees and the remarks that they make about you. More details about the application procedure are given at http://www.st-andrews.ac.uk/admissions/pg/apply/research/

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group (CREEM). Some are open to international students, some to EU and some UK only.

Applicants should have a good first degree in mathematics, statistics or another scientific discipline with a substantial numerical component. Applicants with degrees in other subjects, such as biology, are invited to discuss their qualifications with the Postgraduate Officer. A masters-level degree is an advantage.

Many details of the general requirements and admissions procedure are given at the university web site http://www.st-andrews.ac.uk/admissions/pg/apply/research/

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

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