PARTICLE LEARNING A semester later
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PARTICLE LEARNINGA semester later
Hedibert Freitas Lopes
February 19th 2009.
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Group meetings
Discussion of Storvik and Liu and West (LW) papersCreation of research sub-groupsKernel 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 SessionHedibert Lopes – Particle Learning and Smoothing
Topic Contributed Session – “Particle Learning”Raquel Prado – PL for Autoregressive Models with Structured PriorsChiranjit Mukherjee – PL Without Conditional Sufficient StatisticsChristian Macaro – PL for Long Memory Stochastic Volatility Models
Contributed SessionFrancesca Petralia – PL for Dynamic Stochastic General Equilibrium Models
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