Within the Kolmogorov theory of probability, Bayes’ rule allows one to perform statistical inference by relating conditional probabilities to unconditional probabilities. As we show here, however, there is a continuous set of alternative inference rules that yield the same results, and that may have computational or practical advantages for certain problems. We formulate generalized axioms for ...

Integrating qualitative reasoning with large-scale knowledge bases provides new challenges. This paper outlines work in progress on developing a new model formulation system to support qualitative reasoning via compositional modeling that can operate in an environment with over a million available facts. Three ideas are discussed: Exploiting microtheories for modeling, using non-monotonic infer...

The fuzzy rule based inference is known to be a useful tool to capture the behavior of an approximate system in transportation. One of the obstacles of implementing the fuzzy rule based inference, however, has been to calibrate the membership functions of the fuzzy sets used in the rules. This paper proposes a way to calibrate the membership function when a set of input and output data is given...

Achieving compositional connectionism means finding a way to represent role-filler bindings in a connectionist system without sacrificing role-filler independence. Role-filler binding schemes based on varieties of conjunctive coding (the most common approach in the connectionist literature) fail to preserve role-filler independence. At the same time, dynamic binding of roles to fillers (e.g., b...

To improve the problem that the parameter identification for fuzzy neural network has many time complexities in calculating, an improved T-S fuzzy inference method and an parameter identification method for fuzzy neural network are proposed. It mainly includes three parts. First, improved fuzzy inference method based on production term for T-S Fuzzy model is explained. Then, compared with exist...

This paper explores theoretical issues in constructing an adequate probabilistic semantics for natural language. Two approaches are contrasted. The first extends Montague Semantics with a probability distribution over models. It has nice theoretical properties, but does not account for the ubiquitous nature of ambiguity; moreover inference is NP-hard. An alternative approach is described in whi...

Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy i...

This paper concerns a relationship between Bayes’ inference rule and decision rules from the rough set perspective. In statistical inference based on the Bayes’ rule it is assumed that some prior knowledge (prior probability) about some parameters without knowledge about the data is given first. Next the posterior probability is computed by employing the available data. The posterior probabilit...

Inference is a way to subvert access control mechanisms of database systems. Most existing work on inference detection relies on analyzing functional dependencies in the database schema. This paper is an extension to our earlier e ort in developing a data level inference detection system [Yip and Levitt, 1998]. In this paper, we introduce the split query inference rule, make an extension to the...