Abstract: Logical characterizations of the common prior assumption (CPA) are investigated. Two approaches are considered. The first is called frame distin-guishability, and is similar in spirit to the approaches considered in the economics literature. Results similar to those obtained

The existence of a common prior is a property of the state space used to model the players ’ incomplete information. We show that this property is not just a technical artifact of the model, but that it is immanent to the players ’ beliefs. To this end, we devise a condition, phrased solely

zivyahel @ gmail.com 1 Abstract. Abandoning the oft-presumed common prior assumption, partitioned type spaces with disparate priors are studied. It is shown that in the two-player case, a unique fundamental pair of priors ( 1, 2) p p can be identified in each type space, from whose properties

The existence of a common prior is a property of the state space used to model...

This paper is dedicated to the students of the class “Advanced Topics in Finance ” at Bielefeld University in Summer 2011 We develop the fundamental theorem of asset pricing in a probability– free infinite–dimensional setup. We replace the usual assumption of a prior probability by a certain

In an interesting and useful paper, Frame et al. [2005] (hereinafter F05) call attention to the way in which alternative formulations of a supposedly uninformative prior can significantly affect the results of Bayesian estimation when observational constraints are weak. In particular, using a

Abstract. We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior

The Common Prior Assumption (CPA) is central to the economics of information and the foundations of game theory. Recent contributions (Dekel and Gul, 1997, Gul, 1996, Lipman, 1995) have questioned its meaningfulness in situations of incomplete information where there is no ex ante stage

Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions

The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump- tions made about word occurrences in documents.