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Details of Grant 

EPSRC Reference: EP/C544552/1
Title: Perturbed & self-interacting markov chains: theory & applications to Bayesian inference methodology
Principal Investigator: Andrieu, Professor C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Mathematics
Organisation: University of Bristol
Scheme: Advanced Fellowship (Pre-FEC)
Starts: 01 October 2005 Ends: 30 September 2010 Value (£): 292,211
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
18 Apr 2005 Mathematical Sciences ARF interviews Deferred
14 Mar 2005 Maths Fellowships 2005 Sifting Panel Deferred
Summary on Grant Application Form
In many fields of applied science, some quantities of interest cannot be observed directly, but are distorted and corrupted by noise. In such circumstances, it is needed to infer these quantities from distorted and noisy observations. This requires one to solve complex equations, which do not have an analytic expression, and numerical must be used. A very important type of method are Monte Carlo methods. These methods are very powerful, and have allowed physicists and statisticians to solve numerous problems. However, recently, new challenging problems, in particular in the areas of genomics and proteomics, have shown that current techniques have reached some of their limits. The aim of this project is to develop new computational techniques that could overcome current limitations. There are two approaches considered here: the first approach consists of designing -smart algorithms, that automatically adapt to the task at hand. The second approach consist of approximating an exact algorithm, which generally leads to faster and simpler algorithm. Both novel approaches need to be investigated from both a practical and theoretical point of view, and this is the main topic of this project.
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Organisation Website: http://www.bris.ac.uk