This paper develops a theory for the convergence rates of A* algorithms for real-world vision problems, such as road tracking, which can be formulated in terms of maximizing a reward function derived using Bayesian probability theory. Such problems are well suited to A* tree search and it can be shown that many algorithms proposed to solve them are special cases, or variants, of A*. Moreover, t...

In this paper we examine the empirical findings of belief revision under two alternatives: the Bayesian framework and the Dempster-Shafer theory of belief functions. Bayesian theory is very stringent in its requirement that the probability of mutually exclusive and collectively exhaustive events sum to one. It requires that the belief be increased in light of supporting evidence, and be decreas...

A considerable body of work in AI has been concerned with aggregating measures of confirmatory and disconfirmatory evidence for a common set of propositions. Claiming classical probability to be inadequate or inappropriate, several researchers have gone so far as to invent new formalisms and methods. Some of these have become widely used and theoretically explored [ 17, 15). We show how to repr...

In the preceding paper, Bayesian analysis was applied to the parameter estimation problem, given quadrature NMR data. Here Bayesian analysis is extended to the problem of selecting the model which is most probable in view of the data and all the prior information. In addition to the analytic calculation, two examples are given. The rst example demonstrates how to use Bayesian probability theory...

The Dempster–Shafer theory (DST) may be considered as a generalization of the probability theory, which assigns mass values to the subsets of the referential set and suggests an interval-valued probability measure. There have been several attempts for fuzzy generalization of the DST by assigning mass (probability) values to the fuzzy subsets of the referential set. The interval-valued probabili...

Probability theory can be modified in essentially one way while maintaining consistency with the basic Bayesian framework. This modification results in copies of standard probability theory for real, complex or quaternion probabilities. These copies, in turn, allow one to derive quantum theory while restoring standard probability theory in the classical limit. The argument leading to these thre...

Bayesian inference in medical decision making is a concept that has a long history with 3 essential developments: 1) the recognition of the need for data (demonstrable scientific evidence), 2) the development of probability, and 3) the development of inverse probability. Beginning with the demonstrative evidence of the physician's sign, continuing through the development of probability theory b...