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Lectures on Contingency Tables
, 2002
"... The present set of lecture notes are prepared for the course “Statistik 2” at the University of Copenhagen. It is a revised version of notes prepared in connection with a series of lectures at the Swedish summerschool in Särö, June 11–17, 1979. The notes do by no means give a complete account of the ..."
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The present set of lecture notes are prepared for the course “Statistik 2” at the University of Copenhagen. It is a revised version of notes prepared in connection with a series of lectures at the Swedish summerschool in Särö, June 11–17, 1979. The notes do by no means give a complete account of the theory of contingency tables. They are based on the idea that the graph theoretic methods in Darroch, Lauritzen and Speed (1978) can be used directly to develop this theory and, hopefully, with some pedagogical advantages. My thanks are due to the audience at the Swedish summerschool for patiently listening to the first version of these lectures, to Joseph Verducci, Stanford, who read the manuscript and suggested many improvements and corrections, and to Ursula Hansen, who typed the manuscript.
Permutation Models for Relational Data
, 2005
"... We here propose an exponential family of permutation models that is suitable for inferring the direction and strength of association among dyadic relational structures. A lineartime algorithm is shown for MCMC simulation of model draws, as is the use of simulated draws for maximum likelihood es ..."
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We here propose an exponential family of permutation models that is suitable for inferring the direction and strength of association among dyadic relational structures. A lineartime algorithm is shown for MCMC simulation of model draws, as is the use of simulated draws for maximum likelihood estimation (MCMCMLE) and/or estimation of Monte Carlo standard errors. We also provide an easily performed maximum pseudolikelihood estimation procedure for the permutation model family, which provides a reasonable means of generating seed models for the MCMCMLE procedure. Use of the modeling framework is demonstrated via an application involving relationships among managers in a hightech firm.
Maximum likelihood fitting of acyclic directed mixed graphs to binary data
 Proceedings of the 26th International Conference on Uncertainty in Artificial Intelligence
, 2010
"... mixed graphs to binary data ..."
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Iterative constrained maximum likelihood estimation via expectation propagation
 in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP
, 2006
"... Expectation propagation defines a family of algorithms for approximate Bayesian statistical inference which generalize belief propagation on factor graphs with loops. As is the case for belief propagation in loopy factor graphs, it is not well understood why the stationary points of expectation prop ..."
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Expectation propagation defines a family of algorithms for approximate Bayesian statistical inference which generalize belief propagation on factor graphs with loops. As is the case for belief propagation in loopy factor graphs, it is not well understood why the stationary points of expectation propagation can yield good estimates. In this paper, given a reciprocity condition which holds in most cases, we provide a constrained maximum likelihood estimation problem whose critical points yield the stationary points of expectation propagation. Expectation propagation may then be interpreted as a nonlinear block Gauss Seidel method seeking a critical point of this optimization problem. 1.
Building Inferentially Tractable Models of Complex Social Systems: a Generalized Location Framework
, 2005
"... Here, a class of structures called "generalized location systems" is introduced, which can be used to characterize a range of social processes. ..."
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Here, a class of structures called "generalized location systems" is introduced, which can be used to characterize a range of social processes.