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Conferências

Colegas
O programa de pós-graduação em estatística do IME-USP tem a satisfação de convidá-lo para as conferências dos professores Hedibert Lopes (15 horas) e Michael Höhle (16 horas) 
na próxima dia 01/12 (segunda-feira), sala 136 do bloco A do IME.

Atenciosamente,

Fábio Machado e
Márcia Branco

Seguem os resumos.
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Dr. Hedibert Freitas Lopes
University of Chicago

Title:  Particle Learning and Smoothing
ABSTRACT: This paper provides novel particle learning (PL) methods for sequential filtering, parameter learning and smoothing in a general class of state space models. The approach extends existing particle methods by incorporating unknown fixed parameters, utilizing sufficient statistics, for the parameters and/or the states, and allowing for nonlinearities in the model. We also show how to solve the state smoothing problem, integrating out parameter uncertainty. We show that our algorithms outperform MCMC, as well as existing particle filtering algorithms. 
This paper is joint work with C.Carvalho, M.Johannes and N.Polson.
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Dr Michael Höhle
Department of Statistics, Ludwig-Maximilians-
University Munique, Alemanha.

Title: Stochastic Spatio-Temporal Epidemic Modelling
ABSTRACT: Mathematical modelling is an important tool in order to better understand the dynamics of infectious diseases - be it in human or animals. A key epidemic model is here the stochastic susceptible-exposed-infectious (SIR) model.
This presentation discusses work on extending the SIR modelling with additional focus on the spatial dimension by attempting a regression view on disease dynamics. A continuous time stochastic process model based on conditional intensities is proposed, where dynamics is split into epidemic and endemic components. Simulation from the model using Ogata's modified thinning algorithm is discussed together with likelihood 
based model inference and residual analysis.
As illustration, data provided by the Federal Research Centre for Virus Diseases of Animals, Wusterhausen, Germany, on the incidence of 
classical swine fever virus in Germany during 1993-2004 are analysed.