Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Bayesian Networks and Missing-Data Imputation∗

A decision maker (DM) tries to learn an objective joint probability distribution  over  variables. He gathers many independent observations with (randomly, independently generated) missing values. The DM wishes to extend his incomplete observations into a fully specified, "rectangular" dataset. He employs an iterative imputation procedure, whose individual rounds are akin to the "random regre...

Inferring gene networks from time series microarray data using dynamic Bayesian networks

Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria f...

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing.

Learning Bayesian Networks in Presence of Missing Data

Motivation: Signal Transduction Networks: The study of Signal Transduction Networks is one of the major subjects of interest in Systems Biology. Signal Transduction pathways are means of regulating numerous cellular functions in response to changes in the cell's chemical or physical environment. Signal transduction often involves a sequence of biochemical reactions inside the cell, which are ca...

Inferring Regulatory Pathways in E. Coli using Dynami Bayesian Networks