Online Bayesian Inference of Diffusion Networks

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

Open Problems for Online Bayesian Inference in Neural Networks

Recently, a number of approaches have been developed which allow for approximate Bayesian inference over the parameters of large neural networks. These algorithms can be more robust against overfitting, and can provide useful estimates of model uncertainty. In domains such as active learning Golovin et al. (2010) and Bayesian reinforcement learning (Vlassis et al., 2012), an ideal learner needs...

Inference in Bayesian Networks :

Three kinds of independence are of interest in the context of Bayesian networks, namely conditional independence, independence of causal in uence, and context-speci c independence. It is well-known that conditional independence enables one to factorize a joint probability into a list of conditional probabilities and thereby renders inference feasible. It has recently been shown that independenc...

Inference in Bayesian Networks

In rare cases, primarily involving terminological information or other artificially constructed domains, one has the opportunity to determine categorically the truth of a proposition based on prior knowledge and current observation. Often, truth is more elusive, and categorical statements can only be made by judgment of the likelihood or other ordinal attribute of competing propositions. Probab...

Inference in Bayesian networks