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 …

To understand complex biological systems requires the integration of experimental and
computational research—in other words a systems biology approach. Computational …

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a
challenging combinatorial problem, owing to the intractable search space superexponential …

The aim of this book is to outline the recent development of Markov chain models and their
applications in queueing systems, manufacturing systems, remanufacturing systems …

Motivation: Network inference algorithms are powerful computational tools for identifying
putative causal interactions among variables from observational data. Bayesian network …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

Recently directed acyclic graph (DAG) structure learning is formulated as a constrained
continuous optimization problem with continuous acyclicity constraints and was solved …

This paper re-examines a continuous optimization framework dubbed NOTEARS for
learning Bayesian networks. We first generalize existing algebraic characterizations of …

In this paper, learning a Bayesian network structure that optimizes a scoring function for a
given dataset is viewed as a shortest path problem in an implicit state-space search graph …

The enormous complexity of biological systems at the molecular level must be answered
with powerful computational methods. Computational biology is a young field, but has seen …