Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

اسناد مشابه

• Dynamic Bayesian Networks Modeling for Inferring Genetic Regulatory Networks by Search Strategy: Comparison between Greedy Hill Climbing and MCMC Methods

  سند علمی۲۰۰۹Huihai Wu, Xiaohui Liu

  Using Dynamic Bayesian Networks (DBN) to model genetic regulatory networks from gene expression data is one of the major paradigms for inferring the interactions among genes. Averaging a collection of models for predicting network is desired, rather than relying on a single high

• Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method

  سند علمی۲۰۱۷OncotargetBin Yu, Jia-Meng Xu, Shan Li, Cheng Chen

  Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model h

• Learning dynamic Bayesian network models via cross-validation

  سند علمی۲۰۰۵Pattern Recognition LettersJosé M. Peña, Johan Björkegren, Jesper Tegnér

  We study cross-validation as a scoring criterion for learning dynamic Bayesian network models that generalize well. We argue that cross-validation is more suitable than the Bayesian scoring criterion for one of the most common interpretations of generalization. We confirm this by

• Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning

  سند علمی۲۰۱۳IDAGhada Trabelsi, Philippe Leray, Mounir Ben Ayed, Adel M. Alimi

  Dynamic Bayesian networks (DBNs) are a class of probabilistic graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. The temporal probabilistic graphical models as 2TBN are the most used and popular models for DBNs. Because of the

• Using a Dynamic Bayesian Network to Learn Genetic Interactions

  سند علمی۲۰۰۲Linus Göransson, Timo Koski

  In this paper, we evaluate a method for analyzing microarray data. The method is an attempt to learn regulatory interactions between genes from gene expression data. It is based on a Bayesian network, which is a mathematical tool for modeling conditional independences between sto

• Dynamic Bayesian network modeling for longitudinal brain morphometry

  سند علمی۲۰۱۲NeuroImageRong Chen, Susan M. Resnick, Christos Davatzikos, Edward Herskovits

  Identifying interactions among brain regions from structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We propose a Bayesian data-mining approach to the detection of longitudinal morphological changes in the human brain. Our met