Bayesian approaches to data analysis are popular in machine vision, and yet the main advantage of Bayes theory, the ability to incorporate prior knowledge in the form of the prior probabilities, may lead to problems in quantitative tasks. In this paper we demonstrate examples of Bayesian and nonBayesian techniques with the use of selected examples from the area of magnetic resonance image (MRI)...

This paper compares Bayesian decision theory with robust decision theory where the decision maker optimizes with respect to the worst state realization. For a class of robust decision problems there exists a sequence of Bayesian decision problems whose solution converges towards the robust solution. It is shown that the limiting Bayesian problem displays infinite risk aversion and that decision...

We try to show that Discriminant Analysis can be considered as a branch of Statistical Decision Theory when viewed from a Bayesian approach. First we present the necessary measure theory results, next we briefly outline the foundations of Bayesian Inference before developing Discriminant Analysis as an application of Bayesian Estimation. Our approach renders Discriminant Analysis more flexible ...

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate motion flows in the image sequences. Our theory is expressed in terms of the Bayesian generalization [10] of standard Kalman Filtering which allows us to solve temporal grouping in conjunction with ...

We give here a comparison of the expected outcome theory, the expected utility theory, and the Bayesian decision theory, by way of a simple numerical toy problem in which we look at the investment willingness to avert a high impact low probability event. It will be found that for this toy problem the modeled investment willingness under the Bayesian decision theory is minimally three times high...

Bayesian implementation concerns decision making problems when agents have incomplete information. A recent work [Wu, Quantum mechanism helps agents combat " bad " social choice rules. Intl. J. of Quantum Information 9 (2011) 615-623] generalized the implementation theory with complete information to a quantum domain. In this paper, we propose a quantum Bayesian mechanism and an algorithmic Bay...

This paper introduces the foundations of Bayesian probability theory and Bayesian decision method. The main goal of Bayesian decision theory is to minimize the expected loss of a decision or minimize the expected risk. The purposes of this study are to review the decision process on the issue of flood occurrences and to suggest possible process for decision improvement. This study examines the ...

Bayesian approaches to data analysis are popular in machine vision, and yet the main advantage of Bayes theory, the ability to incorporate prior knowledge in the form of the prior probabilities, may lead to problems in some quantitative tasks. In this paper we demonstrate examples of Bayesian and non-Bayesian techniques from the area of magnetic resonance image (MRI) analysis. Issues raised by ...

The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modeled by means of causal-independence theory, as a theory of continuous causal independe...

Bayesian decision theory is a mathematical framework that models reasoning and decision-making under uncertainty. Around 1990, perceptual psychologists began constructing detailed Bayesian models of perception. 1 This research program has proved enormously fruitful. As two leading perceptual psychologists put it, “Bayesian concepts are transforming perception research by providing a rigorous ma...