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PARTICLE LEARNING A semester later Hedibert Freitas Lopes February 19 th 2009.

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Group meetings Discussion of Storvik and Liu and West (LW) papers Creation of research sub-groups Kernel choice in LW scheme (Petris) APF, SIR & LW (Lopes) Nonlinear PL (Polson) LW + jittering SS (Fearnhead) SMC for long memory time series models (Macaro) SMC for DSGE models (Petralia) PL in structured AR models (Prado) Adaptive SMC in Mixture Analysis (Taylor) SMC for long memory time series models (Macaro)

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Sequential Importance Sampling

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Particle degeneracy

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PL scheme

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No degeneracy

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Resample-propagate or propagate-resample?

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Sufficient statistics

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PL versus LW

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PL versus MCMC

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Smoothing

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PROJECT 1: PL in structured AR models Prado & Lopes (2009)

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PROJECT 2: SMC in LMSV models Macaro & Lopes (2009)

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PROJECT 3: Combining PL and LW Petralia, Hao, Carvalho and Lopes (2009) DGSE : Dynamic General Stochastic Equilibrium

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PROJECT 4: PL in DGSE models Niemi, Chiranjit, Carvalho & Lopes (2009)

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PROJECT 5: PL in epidemic SEIR models Dukic, Lopes & Polson (2009) SEIR: susceptible exposed infected recovered

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PROJECT 6: PL in dynamic factor models Lopes (2009)

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Joint Statistical Meetings 2009 Invited Session Hedibert Lopes – Particle Learning and Smoothing Topic Contributed Session – Particle Learning Raquel Prado – PL for Autoregressive Models with Structured Priors Chiranjit Mukherjee – PL Without Conditional Sufficient Statistics Christian Macaro – PL for Long Memory Stochastic Volatility Models Contributed Session Francesca Petralia – PL for Dynamic Stochastic General Equilibrium Models

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Other projects

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