Recent papers in the field of Finite Element Model (FEM) updating have highlighted the benefits of Bayesian techniques. The Bayesian approaches are designed to deal with the uncertainties associated with complex systems, which is the main problem in the development and updating of FEMs. This paper highlights the complexities and challenges of implementing any Bayesian method when the analysis i...

This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on Jeffreys’ prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated...

Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learning method that optimizes the bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees with those of the bayesian net...

We develop a variational inference (VI) scheme for the recently proposed Bayesian kernel support vector machine (SVM) and a stochastic version (SVI) for the linear Bayesian SVM. We compute the SVM’s posterior, paving the way to apply attractive Bayesian techniques, as we exemplify in our experiments by means of automated model selection.

Bayesian methods have proven to be powerful tools for computed tomographic reconstruction in realistic physical problems. However, Bayesian methods require that a number of modeling and computational problems be addressed. This paper summarizes a coherent system of statistical modeling and optimization techniques designed to facilitate efficient, unsupervised Bayesian emission and transmission ...

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural normalization in the likelihood function, and then follow standard Gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement...

A Bayesian approach to meat quality analyses is discussed and compared with the classical statistical approach. Inferences from means, medians and modes of marginal posterior distributions are presented, and a variety of probability inferences and confidence intervals are presented and discussed. Classical and Bayesian theories of hypothesis testing are compared and their advantages and disadva...

In this paper, we propose a way to derive constraints for a Bayesian Network from structured arguments. Argumentation and Bayesian networks can both be considered decision support techniques, but are typically used by experts with different backgrounds. Bayesian network experts have the mathematical skills to understand and construct such networks, but lack expertise in the application domain; ...

This paper uses Bayesian modeling techniques to analyze a data set extracted from the British General Household survey. The models used are Bayesian networks, which provide a compact and easy to interpret knowledge representation formalism. An issue considered is the need for automated Bayesian modeling.