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Details of Grant 

EPSRC Reference: EP/K000276/1
Title: Advanced Monte Carlo Methods for Inference in Complex Dynamic Models
Principal Investigator: Doucet, Professor A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Statistics
Organisation: University of Oxford
Scheme: EPSRC Fellowship
Starts: 31 March 2013 Ends: 30 March 2018 Value (£): 619,739
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
01 Aug 2012 EPSRC Mathematics Fellowships - August 2012 Announced
04 Jul 2012 Mathematics Prioritisation Panel Meeting July 2012 Announced
Summary on Grant Application Form
Many physical phenomena and much data can be accurately modelled using statistical or proba-

bilistic models. Examples include the volatility of the stock market, gene expressions, radar signals,

relational data or images. However, even if it is possible to obtain realistic physical models or satisfactory

statistical models, only the simplest can be solved without the use of numerical methods.

Examples of the need for such numerical methods include non-linear non-Gaussian time series models,

Markov random fields, social networks models and so on. Thanks to the advent of enormous,

cheap computational power and the development of a plethora of complex inference mechanisms, it

is now possible, and in many real world systems it is becoming increasingly common, to employ

sophisticated simulation-based techniques to provide solutions to problems previously deemed

insoluble. The intention behind the research program discussed herein is to extend current, and devise

novel, simulation-based architectures to attack and efficiently solve problems that are still deemed

intractable.
Key Findings
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Organisation Website: http://www.ox.ac.uk