Relevant material and environmental parameters are required in modelling chloride ingress into concrete. They could be determined from experimental data (concrete cores taken during inspection) but in practice data availability is limited by time-consuming and expensive tests. Consequently, the main objective of this paper is to develop an approach based on Bayesian networks (BN) to improve the...

Epidemiological data is traditionally analyzed with very simple techniques. Flexible models, such as neural networks, have the potential to discover unanticipated features in the data. However, to be useful, exible models must have e ective control on over tting. This paper reports on a comparative study of the predictive quality of neural networks and other exible models applied to real and ar...

1 The Bayesian view of the Jury Theorem 1.1 Criticism on the Bayesian view Recall the model setup: let s ∈ {±1} be the true state of the world. We shall assume a uniform prior on s, that is, P (s = +) = P (s = −) =

The application of the Bayesian Structural EM algorithm to learn Bayesian networks for clustering implies a search over the space of Bayesian network structures alternating between two steps: an optimization of the Bayesian network parameters (usually by means of the EM algorithm) and a structural search for model selection. In this paper, we propose to perform the optimization of the Bayesian ...

We present an algorithm for inducing Bayesian networks using feature selection. The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy. We compare the behavior of this selective Bayesian network classiier with that of (a) Bayesian network classiiers ...

Bayesian networks (BNs) offer a compact, intuitive, and efficient graphical representation of uncertain relationships among the variables in a domain and have proven their value in many disciplines over the last two decades. However, two challenges become increasingly critical in practical applications of Bayesian networks. First, real models are reaching the size of hundreds or even thousands ...

Both learning and inference tasks on Bayesian networks are NP-hard in general. Bounded tree-width Bayesian networks have recently received a lot of attention as a way to circumvent this complexity issue; however, while inference on bounded tree-width networks is tractable, the learning problem remains NP-hard even for tree-width 2. In this paper, we propose bounded vertex cover number Bayesian ...