A fundamental artificial intelligence challenge is how to design agents that intelligently trade off exploration and exploitation while quickly learning about an unknown environment. However, in order to learn quickly, we must somehow generalize experience across states. One promising approach is to use Bayesian methods to simultaneously cluster dynamics and control exploration; unfortunately, ...

The Bayesian approach has a number of attractive properties for forecasting uncertainty which have yet to be fully explored in the study of future population change. In this paper, we apply some simple Bayesian time series models to obtain future population estimates with uncertainty for England and Wales. Uncertainty in model choice is incorporated through Bayesian model averaging techniques. ...

Our objective is to study the contribution of naive increased Bayesian networks in problems of image classification. The images used in this study represent the structure of a document containing text blocks and graphics. We proposed three variants of Bayesian networks. First naive Bayesian networks RN who, despite their simple structure and strong assumption on independence have given very goo...

Various machine learning techniques have been proposed for the development of prognostic models, including those based on Bayesian networks. An advantage of a Bayesian network compared to many other classifiers is that the model can provide insight by representing the temporal structure of the domain. While it has been shown that Bayesian networks can perform well in terms of classification acc...

Over the last decade, the Bayesian approach has increased in popularity in many application areas. It uses a probabilistic framework which encodes our beliefs or actions in situations of uncertainty. Information from several models can also be combined based on the Bayesian framework to achieve better inference and to better account for modeling uncertainty. The approach we adopted here is to u...

Under the subject expected utility paradigm, decisions are made by finding the alternative with the maximum expected utility. In Bayesian simulation, the probability distribution used is the distribution of a simulation output. While methods have been developed under the Bayesian paradigm for choosing the best simulated system from a discrete, finite set of alternatives, the only methods for op...

The Bayesian Optimization Algorithm (BOA) is an algorithm based on the estimation of distributions. It uses techniques from modeling data by Bayesian networks to estimating the joint distribution of promising solutions. To obtain the structure of Bayesian network, different search algorithms can be used. The key point that BOA addresses is whether the constructed Bayesian network could generate...

Recently, papers have appeared that champion the Bayesian approach to the analysis of experimental data. From reading these papers, the physicist could be forgiven for believing that Bayesian methods reveal deep truths about physical systems and are the correct paradigm for the analysis of all experimental data. This paper makes a contrary argument and is deliberately provocative. It is argued ...

Causal relationships are present in many application domains. CP-logic is a probabilistic modeling language that is especially designed to express such relationships. This paper investigates the learning of CP-theories from examples, and focusses on structure learning. The proposed approach is based on a transformation between CP-logic theories and Bayesian networks, that is, the method applies...