Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

Bayesian Networks are probabilistic graphical models that represent conditional
independence relationships among variables as a directed acyclic graph (DAG), where …

This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …

In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that
possess unique methodical features to model dependencies in complex networks, such as …

bnlearn is an R package (R Development Core Team 2010) which includes several
algorithms for learning the structure of Bayesian networks with either discrete or continuous …

Information diffusion and virus propagation are fundamental processes taking place in
networks. While it is often possible to directly observe when nodes become infected with a …

Graphical models provide visual representations of the qualitative structure of our beliefs
between collections of random quantities. Bayesian Networks are directed acyclic graphical …

Real world entities work in concert as a system and not in isolation. Understanding the
associations between these entities from their digital signatures can provide novel system …

Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-
Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and …