| EPSRC Reference: |
EP/J01365X/1 |
| Title: |
Sequential Monte Carlo methods for applications in high dimensions. |
| Principal Investigator: |
Fearn, Professor T |
| Other Investigators: |
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| Department: |
Statistical Science |
| Organisation: |
University College London |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
01 July 2012 |
Ends: |
30 June 2013 |
Value (£): |
98,688
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
Sequential Monte Carlo (SMC) methods are nowadays routinely employed across a wide range of disciplines to calibrate mathematical models and carry out forecasting about complex non-linear stochastic systems, using information from incoming data. SMC methods have been successfully applied in such diverse areas as econometrics, communications, target tracking, computer vision, roboting and biology. They will infer about unknown parameters of stochastic systems and unobserved states of the systems (the "signal") given streams of data.
However, it is a common knowledge that standard SMC methods cannot tackle important high-dimensional problems, arising in fields such as atmospheric sciences, oceanography, hydrology and signal processing, as their computational cost has been found to increase exponentially fast with the dimension of the state space of the system.
The proposed research will investigate and develop advanced SMC methods of improved algorithmic efficiency in high dimensions, rendering SMC methodology practically relevant in such contexts. This is of high importance as alternative methods currently used in high dimensions cannot fully capture non-linear model dynamics arising in applications, and can give inaccurate estimates of uncertainty or forecasts in such non-linear scenarios.
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Key Findings |
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No information has been submitted for this grant.
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Potential use in non-academic contexts |
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No information has been submitted for this grant.
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Impacts |
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No information has been submitted for this grant.
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Sectors submitted by the Researcher |
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No information has been submitted for this grant.
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.ucl.ac.uk |