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Sequential Monte Carlo Methods By Prof. Ajay Jasra (National University of Singapore)

  • Class schedule: Tuesday, Oct. 14th,  2014, 11am -12:30pm  &  3 - 4:30pm
  • Location: Building 1, Room 4214
  • Refreshments:  Available @ 10:45 am & @ 2:45pm

This talk will provide a tutorial style introduction to sequential Monte Carlo methods, attempting to start from a basic introduction, to a coverage of some of the latest research developments.
Sequential Monte Carlo methods are applied in a wide variety of applications, including engineering, economics and biology. They combine importance sampling and resampling to approximate distributions. The idea is to introduce a sequence of proposal densities and sequentially simulate a collection of N >1 samples, termed particles, in parallel from these proposals. In most scenarios it is not possible to use the distribution of interest as a proposal. Therefore, one must correct for the discrepancy between proposal and target via importance weights. There are a variety of algorithms and extensions which form some of the most cutting edge methodology for `exact' computations in many academic fields.

Ajay Jasra received his PhD degree in statistics from Imperial College London in 2005.

Since 2011 he has been associate professor at the Department of Statistics and Applied Probability at the National University of Singapore. Between 2005-2008 he has held various post-doctoral positions at the University of Oxford, University of Cambridge and the Institute of Statistical Mathematics in Tokyo. He was also Chapman Fellow of Mathematics at Imperial College London in that period. Between 2008-2011 he was assistant professor at Imperial College London. He is currently associate editor at Statistics and Computing, American Journal of Algorithms and Computing and Stat.