Put simply, Bayesian deep learning adds a prior distribution over each weight and bias parameter found in a typical neural network model. In the past, Bayesian deep learning models were not used very ...

Microarray analysis with Bayesian hierarchical clustering and Bayesian network clustering on three microarray datasets. Pearson's correlation coefficient and an augmented Markov blanket are used for ...

We introduce an algorithm for Bayesian network inference using parallel computations that perform variable-elimination over multiple threads of execution. The algorithm can be implemented on a ...

Structure learning of Bayesian networks is a well-researched but computationally hard task. We present an algorithm that integrates an information theory-based approach and a scoring function-based ...