This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectatio...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). Prior distributions are defined using stochastic logic programs...
Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples...
— In this paper, we present an approach allowing a robot to learn a generative model of its own physical body from scratch using self-perception with a single monocular camera. O...