In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
We develop a framework based on Bayesian model averaging to explain how animals cope with uncertainty about contingencies in classical conditioning experiments. Traditional accoun...
Aaron C. Courville, Nathaniel D. Daw, Geoffrey J. ...
We explore a means to both model and reason about partial observability within the scope of constraintbased temporal reasoning. Prior studies of uncertainty in Temporal CSPs have ...
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...
The problem of coalition formation when agents are uncertain about the types or capabilities of their potential partners is a critical one. In [3] a Bayesian reinforcement learnin...