Modelling structured data with Probabilistic Graphical Models

Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models

Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The analysis of multivariate count data is a recurrent and crucial issue in numerous modelling problems, particularly in the fields of biology and ecology (where the data can represent, for example, children counts associated...

BayesStore: managing large, uncertain data repositories with probabilistic graphical models

Several real-world applications need to effectively manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications must constantly reason about volumes of noisy sensory readings for a variety of reasons, including motion prediction and human behavior modeling. Such probabilistic data analyses require sophisticated machine-learning too...

Probabilistic Graphical Models

P Probabilistic Graphical Models

Bayesian network (BN) A directed graph whose nodes represent variables, and edges represent influences. Together with conditional probability distributions, a Bayesian network represents the joint probability distribution of its variables. Conditional probability distribution Assignment of probabilities to all instances of a set of variables when the value of one or more variables is known. Con...

Visualizing Graphical Probabilistic Models

Complex probabilistic models are difficult to evaluate not only for consistency with the domain theory but also for novelty and significance. In this paper we describe the current visual representations of Bayesian networks and then present an implementation containing enhancements to these visualizations by integrating several well known techniques. We then apply our approach in an exploration...