Dynamic Bayesian Information Measures

Information Measures via Copula Functions

In applications of differential geometry to problems of parametric inference, the notion of divergence is often used to measure the separation between two parametric densities. Among them, in this paper, we will verify measures such as Kullback-Leibler information, J-divergence, Hellinger distance, -Divergence, … and so on. Properties and results related to distance between probability d...

Using Causal Information and Local Measures to Learn Bayesian Networks

In previous work we developed a method of learning Bayesian Network models from raw data This method relies on the well known minimal description length MDL principle The MDL principle is particularly well suited to this task as it allows us to tradeo in a principled way the accuracy of the learned network against its practical usefulness In this paper we present some new results that have aris...

Information importance of predictors: Concept, measures, Bayesian inference, and applications

The importance of predictors is characterized by the extent to which their use reduces uncertainty about predicting the response variable, namely their information importance. The uncertainty associated with a probability distribution is a concave function of the density such that its global maximum is a uniform distribution reflecting the most difficult prediction situation. Shannon entropy is...

Distribution-Invariant Risk Measures, Information, and Dynamic Consistency

In the first part of the article, we characterize distribution-invariant risk measures with convex acceptance and rejection sets on the level of distributions. It is shown that these risk measures are closely related to utility-based shortfall risk. In the second part of the paper, we provide an axiomatic characterization for distributioninvariant dynamic risk measures of terminal payments. We ...

Automatic Speech Recognition using Pitch Information in Dynamic Bayesian Networks

Information Security Risk Assessment under Uncertainty Using Dynamic Bayesian Networks

The risk management process is the key task of every decision maker in an organization. This risk management process should be carried out periodically to review the security of the information assets in the organization. So if this process is to be efficient, the organization should first prioritize the information assets and should employ risk management procedure to avoid potential loss. But...