Cycles in Bayesian Networks

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

 Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization

A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

Cycles in Metabolic Networks

Modeling sophisticated biological systems in a way that makes them more apprehensible, without losing comprehension or robustness, is a challenging task. The flux balance analysis (FBA) model describes metabolic networks of single-celled organisms with the goal of simulating their steady-state behavior. FBA simulations yield reliable and biologically relevant growth rate values, but do not simu...

Cycles in Networks

We study the presence of cycles and long paths in graphs that have been proposed as interconnection networks for parallel architectures. The study surveys and complements known results.

Directed Cycles in Bayesian Belief Networks: Probabilistic Semantics and Consistency Checking Complexity