We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate inference. Specifically we propose to combine the outer-loop step of convergent belief propagation algorithms with the M-step of the EM algorithm. This then yields an approximate EM algorithm that is essentially still d...

In response to the changing terms of competition in business, traditional accounting is becoming AIS – a growing subdiscipline of IS. Visionaries see accounting of the future as primarily an IS discipline as the field changes into a technologydriven information system profession. The challenge for accounting education is to keep pace with the transformation of the profession. This study uses da...

It is assumed that players bundle nodes in which other players must move into analogy classes, and players only have expectations about the average behavior in every class. A solution concept is proposed for multi-stage games with perfect information: at every node players choose best-responses to their analogy-based expectations, and expectations are correct on average over those various nodes...

Technical reports from the Automatic Control group in Linkk oping are available by anonymous ftp at the address ftp.control.isy.liu.se. This report is contained in the compressed postscript le 2067.ps.Z.

Expectation is a central notion in probability theory. The notion of expectation also makes sense for other notions of uncertainty. We introduce a propositional logic for reasoning about expectation, where the semantics depends on the underlying representation of uncertainty. We give sound and complete axiomatizations for the logic in the case that the underlying representation is (a) probabili...

• Distribution 2: Pr(0) = Pr(50) = Pr(100) = 1/3. Both have the same expectation: 50. But the first is much less “dispersed” than the second. We want a measure of dispersion. • One measure of dispersion is how far things are from the mean, on average. Given a random variable X , (X(s) − E(X)) measures how far the value of s is from the mean value (the expectation) of X . Define the variance of ...

We present a general framework containing a graded spectrum of Expectation Maximization (EM) algorithms called Unified Expectation Maximization (UEM.) UEM is parameterized by a single parameter and covers existing algorithms like standard EM and hard EM, constrained versions of EM such as ConstraintDriven Learning (Chang et al., 2007) and Posterior Regularization (Ganchev et al., 2010), along w...

This work introduces Expectation-oriented Modeling (EOM) as a conceptual and formal framework for the modeling and in uencing of blackor gray-box agents and agent interaction from the viewpoint of modelers like arti cial agents and application designers. EOM is unique in that autonomous agent behavior is not restricted in advance, but only if it turns out to be necessary at runtime, and does so...