This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modelling uncertainty in semantic web. The proposed method includes four components: 1) learning probabilities (priors about concepts, conditionals between subconcepts and superconcepts, and raw semantic similarities between c...

The inverse ocean color problem, i.e., the retrieval of marine reflectance from top-of-atmosphere (TOA) reflectance, is examined in a Bayesian context. The solution is expressed as a probability distribution that measures the likelihood of encountering specific values of the marine reflectance given the observed TOA reflectance. This conditional distribution, the posterior distribution, allows ...

Sanger TD. Bayesian filtering of myoelectric signals. J Neurophysiol 97: 1839 –1845, 2007. First published December 20, 2006; doi:10.1152/jn.00936.2006. Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunate...

Subjective probability and Bayesian methods provide a unified approach to handle not only randomness from stochastic sample-paths, but also uncertainty about input parameters and response metamodels. The chapter surveys some basic concepts, principles and techniques useful for a subjective Bayesian approach to uncertainty analysis, data collection plans to reduce input uncertainty, response sur...

Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilayer perceptron). Two technical approaches are proposed for the transformation of...

The standard practice of selecting a single model from some class of models and then making inferences based on this model ignores model uncertainty. Ignoring model uncertainty can impair predictive performance and lead to overstatement of the strength of evidence via p-values that are too small. Bayesian model averaging provides a coherent approach for accounting for model uncertainty. A varie...

This paper is concerned with a development of a theory on probabilistic models, and in particular Bayesian networks, when handling continuous variables. While it is possible to deal with continuous variables without discretisation, the simplest approach is to discretise them. A fuzzy partition of continuous domains will be used, which requires an inference procedure able to deal with soft evide...