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13:00 - 14:00 4 October 2012

Maximum Likelihood Particle Parameter Estimation for State-Space Models


Roberts G06 Sir Ambrose Fleming LT | Roberts Building (link Map)
Malet Place | London | WC1E 7JE | United Kingdom

Open to: Academic | Alumni | Public | Student
Admission: Free of charge
Ticketing: Open

Speaker information

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

In most applications, the state-space model of interest depends on unknown static parameters that need to be inferred from the data either online or offline.

The aim of this lecture is to explain why many naive and sophisticated particle schemes proposed in this context are unreliable in the presence of large datasets. I will then present original approaches to perform online or offline parameter inference. These provably stable schemes rely on an exact forward only implementation of the celebrated forward backward recursion originating from dynamic programming.

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.


Victoria Nicholl
020 7679 0481 | v.nicholl@ucl.ac.uk


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