Quasi-Bayesian theory uses convex sets of probability distributions and expected loss to represent preferences about plans. The theory focuses on decision robustness, i.e., the extent to which plans are aaected by deviations in subjective assessments of probability. Generating a plan means enumerating the actions to be taken and providing information about the robustness of the actions. The pre...

Decision making is an active and purposeful selection of actions among several alternative options. For humans, DM is a natural part of everyday life. The Bayesian theory provides a rigorous and consistent tool to help the decision maker to select the best action to achieve his aim. A significant application area of the decision-making theory is the control theory. Most of the applications of t...

The determination of low-energy constants from data is an important component of most effective field theory programs, including that of chiral perturbation theory. We propose a novel method based on Bayesian probability theory which allows us to address several shortcomings of the standard approach to parameter extraction. Using a toy-model we argue that the Bayesian approach is ideally suited...

What is the powerful ingredient which allows a dramatic speed-up of quantum computation over classical computation ? We propose that this ingredient is an implicit use of the Bayesian probability theory. Furthermore, we argue that both classical and quantum computation are special cases of probability reasoning. On these grounds, introducing Bayesian probability theory in classical computation ...

The process by which the human visual system parses an image into contours, surfaces, and objects--perceptual grouping--has proven difficult to capture in a rigorous and general theory. A natural candidate for such a theory is Bayesian probability theory, which provides optimal interpretations of data under conditions of uncertainty. But the fit of Bayesian theory to human grouping judgments ha...

A Bayesian prior over first-order theories is defined. It is shown that the prior can be approximated, and the relationship to previously studied priors is examined.

To formalise our discussion of model uncertainty we will rely on probabilistic modelling, and more specifically on Bayesian modelling. Bayesian probability theory offers us the machinery we need to develop our tools. Together with techniques for approximate inference in Bayesian models, in the next chapter we will present the main results of this work. But prior to that, let us review the main ...

The Sleeping Beauty problem is test stone for theories about selflocating belief, i.e. theories about how we should reason when data or theories contain indexical information. Opinion on this problem is split between two camps, those who defend the “1/2 view” and those who advocate the “1/3 view”. I argue that both these positions are mistaken. Instead, I propose a new “hybrid” model, which avo...