Humans and other animals behave as if we perform fast Bayesian inference underlying decisions and movement control given uncertain sense data. Here we show that a biophysically realistic model of the subthreshold membrane potential of a single neuron can exactly compute the numerator in Bayes rule for inferring the Poisson parameter of a sensory spike train. A simple network of spiking neurons ...

The analysis of nominal data is often reduced to accumulation and description. Bayesian methods offer a possibility to analyse nominal data in a more sophisticated way. The possibility to indicate a structure via graphical representation, where variables are nodes and relationships are edges, enriches this method and makes it a powerful tool for data analysis. In this paper, an overview on Baye...

We present Bayesian updating of an imprecise probability measure, represented by a class of precise multidimensional probability measures. Choice and analysis of our class are motivated by expert interviews that we conducted with modelers in the context of climatic change. From the interviews we deduce that generically, experts hold a much more informed opinion on the marginals of uncertain par...

Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional pr...

Maintaining semantics for uncertainty is critical during knowledge acquisition. We examine Bayesian KnowledgeBases (BKBs) which are a generalization of Bayesian networks. BKBs provide a highly flexible and intuitive representation following a basic “if-then” structure in conjunction with probability theory. We present theoretical results concerning BKBs and how BKBs naturally and implicitly pre...

We investigate new approaches for knowledge discovery from two medical databases. Two different kinds of knowledge, namely rules and causal structures, are learned. Rules capture interesting patterns and regularities in the database. Causal structures represented by Bayesian networks capture the causality relationships among the attributes. We employ advanced evolutionary algorithms for these d...

Bayesian analysis and modeling, in which uncertainties are quantified in terms of probability, offers an alternative approach to understanding in meteorological applications. The underlying principle, practiced in fields like archaeology and geology, is the accumulation of evidence. The approach provides a mathematical rule to update existing beliefs in light of new evidence. It requires the da...

Although Bayesian model averaging is theoretically the optimal method for combining learned models, it has seen very little use in machine learning. In this paper we study its application to combining rule sets, and compare it with bagging and partitioning, two popular but more ad hoc alternatives. Our experiments show that, surprisingly, Bayesian model averaging’s error rates are consistently ...

The Bayesian logic is generally associated to the definition of a prior probabilistic law. Conditional algebra have been investigated by some authors though, but somehow the background framework is still probabilistic and the entire logic is not specified. In this paper, the definition of a Deterministic Bayesian Logic is proposed. This logic is completely independent of any notion of probabili...

In this research, the decision on belief (DOB) approach was employed to analyze and classify the states of uni-variate quality control systems. The concept of DOB and its application in decision making problems were introduced, and then a methodology for modeling a statistical quality control problem by DOB approach was discussed. For this iterative approach, the belief for a system being out-...