Abstract

http://ssrn.com/abstract=1947050
 
 

References (29)



 
 

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Particle Learning and Smoothing


Carlos Carvalho


University of Texas at Austin - Red McCombs School of Business

Michael S. Johannes


Columbia Business School - Finance and Economics

Hedibert F. Lopes


University of Chicago - Booth School of Business

Nick Polson


University of Chicago - Booth School of Business

2010

Statistical Science, Vol. 25, No. 1, pp. 88-106, 2010

Abstract:     
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.

Number of Pages in PDF File: 19

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Date posted: October 22, 2011 ; Last revised: November 9, 2011

Suggested Citation

Carvalho, Carlos and Johannes, Michael S. and Lopes, Hedibert F. and Polson, Nick, Particle Learning and Smoothing (2010). Statistical Science, Vol. 25, No. 1, pp. 88-106, 2010 . Available at SSRN: http://ssrn.com/abstract=1947050

Contact Information

Carlos Carvalho
University of Texas at Austin - Red McCombs School of Business ( email )
Austin, TX 78712
United States
Michael Slater Johannes
Columbia Business School - Finance and Economics ( email )
3022 Broadway
New York, NY 10027
United States

Hedibert F. Lopes (Contact Author)
University of Chicago - Booth School of Business ( email )
5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
Nick Polson
University of Chicago - Booth School of Business ( email )
5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)
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