Learning the structure of dynamic Bayesian networks from time series and steady state measurements

A model for reasoning about persistence and causation

Computational Intelligence

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning

A tutorial on learning with Bayesian networks

Learning in graphical models

Causality: models, reasoning, and inference

Causality: models, reasoning, and inference

Model selection by MCMC computation

Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing

Probabilistic Networks and Expert Systems

Probabilistic Networks and Expert Systems

Dynamic bayesian networks: representation, inference and learning

Dynamic bayesian networks: representation, inference and learning

Sparse graphical models for exploring gene expression data

Journal of Multivariate Analysis

Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods (Springer Texts in Statistics)

Reconstruction of gene networks using Bayesian learning and manipulation experiments

Bioinformatics

An empirical Bayes approach to inferring large-scale gene association networks

Bioinformatics

Updating Markov Chains with an Eye on Google's PageRank

SIAM Journal on Matrix Analysis and Applications

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

Signal Processing

An introduction to ROC analysis

Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks

Bioinformatics

An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks

Bioinformatics

Learning the structure of dynamic probabilistic networks

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence