Chuanhai Liu
Parameter Expansion and Efficient Inference.
Thu, 04/14/2011 - 15:01 — SomNambulAuthors: Chuanhai Liu, Andrew Lewandowski, Scott Vander Wiel
Subjects: Methodology
AbstractThis EM review article focuses on parameter expansion, a simple technique
introduced in the PX-EM algorithm to make EM converge faster while maintaining
its simplicity and stability. The primary objective concerns the connection
between parameter expansion and efficient inference. It reviews the statistical
interpretation of the PX-EM algorithm, in terms of efficient inference via bias
reduction, and further unfolds the PX-EM mystery by looking at PX-EM from
different perspectives.Dempster--Shafer Theory and Statistical Inference with Weak Beliefs.
Thu, 11/04/2010 - 16:01 — SomNambulAuthors: Chuanhai Liu, Ryan Martin, Jianchun Zhang
Subjects: Methodology
AbstractThe Dempster--Shafer (DS) theory is a powerful tool for probabilistic
reasoning based on a formal calculus for combining evidence. DS theory has been
widely used in computer science and engineering applications, but has yet to
reach the statistical mainstream, perhaps because the DS belief functions do
not satisfy long-run frequency properties. Recently, two of the authors
proposed an extension of DS, called the weak belief (WB) approach, that can
incorporate desirable frequency properties into the DS framework by
systematically enlarging the focal elements.The Dynamic ECME Algorithm.
Tue, 04/06/2010 - 15:01 — SomNambulAuthors: Chuanhai Liu, Yunxiao He
Subjects: Computation
AbstractThe ECME algorithm has proven to be an effective way of accelerating the EM
algorithm for many problems. Recognising the limitation of using prefixed
acceleration subspace in ECME, we propose the new Dynamic ECME (DECME)
algorithm which allows the acceleration subspace to be chosen dynamically. Our
investigation of an inefficient special case of DECME, the classical Successive
Overrelaxation (SOR) method, leads to an efficient, simple, and widely
applicable DECME implementation, called DECME_v1.Estimating limits from Poisson counting data using Dempster--Shafer analysis.
Mon, 08/24/2009 - 13:10 — SomNambulAuthors: Paul T. Edlefsen, Chuanhai Liu, Arthur P. Dempster
Subjects: Applications
AbstractWe present a Dempster--Shafer (DS) approach to estimating limits from Poisson
counting data with nuisance parameters. Dempster--Shafer is a statistical
framework that generalizes Bayesian statistics. DS calculus augments
traditional probability by allowing mass to be distributed over power sets of
the event space. This eliminates the Bayesian dependence on prior distributions
while allowing the incorporation of prior information when it is available. We
use the Poisson Dempster--Shafer model (DSM) to derive a posterior DSM for the
``Banff upper limits challenge'' three-Poisson model.
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