贝叶斯网络 R语言实例牛津大学.pdf

发布时间：2022-06-06 发布人：admin 分类：说明书资料大小：2.79M 资料格式：pdf 举报版权申诉

qq_43985998-12556207-16359647292549471314.pdf-第1页.png

第1页 / 共322页

qq_43985998-12556207-16359647292549471314.pdf-第2页.png

第2页 / 共322页

qq_43985998-12556207-16359647292549471314.pdf-第3页.png

第3页 / 共322页

qq_43985998-12556207-16359647292549471314.pdf-第4页.png

第4页 / 共322页

qq_43985998-12556207-16359647292549471314.pdf-第5页.png

第5页 / 共322页

qq_43985998-12556207-16359647292549471314.pdf-第6页.png

第6页 / 共322页

qq_43985998-12556207-16359647292549471314.pdf-第7页.png

第7页 / 共322页

qq_43985998-12556207-16359647292549471314.pdf-第8页.png

第8页 / 共322页

Definitions

Fundamentals of Inference

Advanced Inference

Fundamentals of Structure Learning

Advanced Structure Learning, Parameter Learning

Hands-On Examples

That's It, Thanks!

Understanding Bayesian Networks with Examples in R Marco Scutari scutari@stats.ox.ac.uk Department of Statistics University of Oxford January 23–25, 2017

Deﬁnitions Marco Scutari University of Oxford

Deﬁnitions A Graph and a Probability Distribution Bayesian networks (BNs) are deﬁned by: a network structure, a directed acyclic graph G = (V, A), in which each node vi ∈ V corresponds to a random variable Xi; a global probability distribution X with parameters Θ, which can be factorised into smaller local probability distributions according to the arcs aij ∈ A present in the graph. The main role of the network structure is to express the conditional independence relationships among the variables in the model through graphical separation, thus specifying the factorisation of the global distribution: N P(X) = P(Xi | ΠXi; ΘXi) where ΠXi = {parents of Xi} i=1 Marco Scutari University of Oxford

Deﬁnitions Where to Look: Book References (Best perused as ebooks, the Koller & Friedman is ≈ 21/2 inches thick.) Marco Scutari University of Oxford

Deﬁnitions How to Use: Software References DISCLAIMER: I am the author of the bnlearn R package and I will use it for the most part in this course. install.packages("bnlearn") For displaying graphs, I will use the Rgraphviz from BioConductor: source("http://bioconductor.org/biocLite.R") biocLite(c("graph", "Rgraphviz")) For exact inference on discrete Bayesian networks: source("http://bioconductor.org/biocLite.R") biocLite(c("graph", "Rgraphviz", "RBGL")) install.packages("gRain") Other packages from CRAN: install.packages(c("pcalg", "catnet", "abn")) Marco Scutari University of Oxford

Deﬁnitions Graphs The ﬁrst component of a BN is a graph. A graph G is a mathematical object with: a set of nodes V = {v1, . . . , vN}; a set of arcs A which are identiﬁed by pairs for nodes in V, e.g. aij = (vi, vj). Given V, a graph is uniquely identiﬁed by A. The arcs in A can be: undirected if (vi, vj) is an unordered pair and the arc vi − vj has no direction; directed if (vi, vj) = (vj, vi) is an ordered pair and the arc has a speciﬁc direction vi → vj. The assumption is that there is at most one arc between a pair of nodes. Marco Scutari University of Oxford EABCDABCDE

Deﬁnitions Directed Acyclic Graphs BNs use a speciﬁc kind of graph called a directed acyclic graph, that: contains only directed arcs; does not contain any loop (e.g. an arc vi → vi from a node to does not contain any cycle (e.g. a sequence of arcs itself); vi → vj → . . . → vk → vi that starts and ends in the same node). Marco Scutari University of Oxford ABCDEABCDEABCDE

Deﬁnitions How the DAG Maps to the Probability Distribution Formally, the DAG is an independence map of the probability distribution of X, with graphical separation (⊥⊥G) implying probabilistic independence (⊥⊥P ). Marco Scutari University of Oxford CABDEFDAGGraphicalseparationProbabilisticindependence

分享到：

赞收藏

资料库

贝叶斯网络 R语言实例牛津大学.pdf

相关推荐

信息化

热门标签

最新资料