‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...

SLAM: cross-species gene finding and alignment with a generalized pair hidden Markov model. Bayesian approach to reconstructing genetic regulatory networks with hidden factors. to analyze expression data. Estimation of genetic networks and functional structures between genes by using Bayesian networks and non-parametric regression. network and nonparametric heteroscedastic regression for nonlin...

BAYESIAN NONPARAMETRIC MODELING OF EPILEPTIC EVENTS

Citation: Chetty M, Cain T, Wedagedera J, Rostami-Hodjegan A and Jamei M (2018) Application of Physiologically Based Pharmacokinetic (PBPK) Modeling Within a Bayesian Framework to Identify Poor Metabolizers of Efavirenz (PM), Using a Test Dose of Efavirenz. Front. Pharmacol. 9:247. doi: 10.3389/fphar.2018.00247 Application of Physiologically Based Pharmacokinetic (PBPK) Modeling Within a Bayesi...

Analysis of a real case of software troubleshooting made it clear that probabilistic models, or Bayesian networks are suitable for modeling software troubleshooting processes in today’s computing environments. As a way to support software troubleshooting by reusing skillful support engineers’ expertise, I propose to use Bayesian networks and probabilistic reasoning for case-based retrieval. The...

Intelligent user assistance systems face incomplete, uncertain and multiple modality sensory observations, user’s dynamically changing internal state, and many constraints in making decisions. We introduce a new probabilistic framework based on the dynamic Bayesian networks to dynamically and stochastically model the user’s internal state, profile, the sensory observations, and the contextual i...

The modeling of critical infrastructure interdependencies is a challenging task. This paper discusses several interdependency modeling requirements and proposes a Bayesian network approach for modeling interdependencies and inoperability propagation. The approach is applied to a case study involving the Japanese critical infrastructure sectors. Survey data published by the National Institute of...

Estimation of growth curves of poultry species is of particular importance in animal science. This study aims at fitting hierarchical Gompertz growth curve to Japanese quails body weight data obtained from hatching to 56 days of age weekly. The model involved a random animal effect as well as sex and line effects in asymptotic weight parameters. Model parameters were estimated via Bayesian meth...

Recent research into reconstructing biological networks has examined the use of dynamic Bayesian networks to model time-series data. While intuitively appealing, dynamic Bayesian network modeling makes assumptions about the properties of time-series data which may not hold for sparsely sampled datasets. This work argues that static Bayesian networks may be a more appropriate model for such data...

As Bayesian networks are applied to larger and more complex problem domains, search for flexible modeling and more efficient in­ ference methods is an ongoing effort. Mul­ tiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference for Bayesian networks into a coherent framework for flexible modeling and distributed inference. Lazy propagation extends the Shafer-Shenoy and HUG IN infer...