We present node level primitives for parallel exact inference on an arbitrary Bayesian network. We explore the probability representation on each node of Bayesian networks and eac...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
A student's goals and attitudes while interacting with a tutor are typically unseen and unknowable. However their outward behavior (e.g. problem-solving time, mistakes and hel...
This paper presents a technique for an intelligent robot to adaptively behave in unforeseen and dynamic circumstances. Since the traditional methods utilized the relatively reliabl...
This paper describes a novel method for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is m...