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13:00 - 14:00 3 October 2012
Particle Filtering and Smoothing for State-Space Models
Roberts G06 Sir Ambrose Fleming LT |
Malet Place | London | WC1E 7JE | United Kingdom
Admission: Free of Charge - bookings via link
Arnaud Doucet, Professor of Statistics, University of Oxford, Arnaud Doucet obtained his PhD Degree from University Paris XI in 1997. He has held previously faculty positions at the University of Melbourne, the University of Cambridge, the Institute of Statistical Mathematics in Tokyo and was a Canada Research Chair at the University of British Columbia. He joined the Department of Statistics of the University of Oxford in 2011 where he is currently Professor. He is Associate editor of the Annals of Statistics and ACM Transactions on Modeling and Computer Simulation. His research areas include Monte Carlo methods, Bayesian statistics, dynamic models and their applications
State-space models are a popular class of time series models which are ubiquitous in econometrics, ecology, robotics, signal processing, statistics etc.
Beyond finite state-space and linear Gaussian models, approximate inference in state-space models relies either on analytical or numerical approximations of the posterior distributions of interest.
Particle methods are a class of sequential Monte Carlo methods which are flexible, easily parallelizable and provide consistent estimates. In this talk, I will review standard and advanced particle filtering and smoothing techniques. I will also discuss theoretical results which shed light on the performance of these approaches.
Lunch will follow the lecture & Arnaud will be available for individual of group meetings during the day from Monday 1 to Friday 5 October 2012. Please email Victoria Nicholl firstname.lastname@example.org
020 7679 0481 | email@example.com
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