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Activity Number: 552
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #318988 View Presentation
Title: Sequential Monte Carlo Smoothing with Parameter Estimation
Author(s): Biao Yang* and Jonathan Stroud and Gabriel Huerta
Companies: The George Washington University and Georgetown University and University of New Mexico
Keywords: particle smoothing ; particle filtering ; particle learning smoothing ; state-space models

In this talk, we propose new smoothing methods for state-space models with unknown parameters. The first approach originates from the particle smoothing algorithm, but with an adjustment in the backward resampling weights. The second one is a new method combining parameter estimation and smoothing for general state-space models. The method is straightforward but highly efficient. Using simulated and real data, we show that this is the best existing method using Sequential Monte Carlo to solve the joint Bayesian smoothing problem.

Authors who are presenting talks have a * after their name.

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