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