bnlearn - Bayesian network structure learning
Features bnlearn provides an open implementation of large parts of the literature on Bayesian networks: Classes of Bayesian networks: discrete (multinomial) Bayesian networks for discrete data, Gaussian Bayesian networks for continuous data and Conditional Gaussian networks for mixed data. Structure learning algorithms: constraint-based (PC Stable, Grow-Shrink, IAMB, Fast-IAMB, Inter-IAMB, IAMB-FD
bnlearn - Bayesian network structure learning
Bayesian Networks in R with Applications in Systems Biology R. Nagarajan, M. Scutari and S. Lèbre (2013). Use R!, Vol. 48, Springer (US). ISBN-10: 1461464455 ISBN-13: 978-1461464457 Springer Website Amazon Website Errata Corrige page 3: “if a node vi precedes vj, there can be no arc from vj to vi” should be “if a node vi precedes vj, there can be no path from vj to vi”. page 3: it's true that leaf
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
bnlearn - Tutorial for useR! 2019 in Toulouse.
bnlearn - Bayesian Network Repository
Learning Bayesian Networks in R - an Example in Systems Biology
