PARTICLE LEARNING A semester later Hedibert Freitas Lopes February 19 th 2009.
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PARTICLE LEARNING A semester later Hedibert Freitas Lopes February 19 th 2009.
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Transcript of PARTICLE LEARNING A semester later Hedibert Freitas Lopes February 19 th 2009.
PARTICLE LEARNINGA semester later
Hedibert Freitas Lopes
February 19th 2009.
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)
Sequential Importance Sampling
Particle degeneracy
PL scheme
No degeneracy
Resample-propagate or propagate-resample?
Sufficient statistics
PL versus LW
PL versus MCMC
Smoothing
PROJECT 1: PL in structured AR models
Prado & Lopes (2009)
PROJECT 2: SMC in LMSV models
Macaro & Lopes (2009)
PROJECT 3: Combining PL and LW
Petralia, Hao, Carvalho and Lopes (2009)DGSE : Dynamic General Stochastic Equilibrium
PROJECT 4: PL in DGSE models
Niemi, Chiranjit, Carvalho & Lopes (2009)
PROJECT 5: PL in epidemic SEIR models
Dukic, Lopes & Polson (2009)SEIR: susceptible exposed infected recovered
PROJECT 6: PL in dynamic factor models
Lopes (2009)
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
Other projects
