PyGModels: A Python package for exploring Probabilistic Graphical Models with Graph Theoretical Structures

Package Planning with Graphical Models

We consider a novel class of applications where a set of activities conducted by a group of people over a time period needs to be planned, taking into account each member’s preference. We refer to the decision process that leads to such a plan as package planning. The problem differs from a number of well-studied AI problems including standard AI planning and decision-theoretic planning. We pre...

Inference in Probabilistic Graphical Models by Graph Neural Networks

A useful computation when acting in a complex environment is to infer the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence a...

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...