Bayesian Learning of Clique Tree Structure
The problem of categorical data analysis in high dimensions is considered. A discussion of the fundamental difficulties of probability modeling is provided, and a solution to the derivation of high dimensional probability distributions based on Bayesian learning of clique tree decomposition is presented. The main contributions of this paper are an automated determination of the optimal clique tree structure for probability modeling, the resulting derived probability distribution, and a corresponding unified approach to clustering and anomaly detection based on the probability distribution.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionClusteringTree DecompositionSimilar Papers 제목 키워드 기반
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees
Exact inference in Bayesian networks is intractable and has an exponential dependence on the size of the largest clique in the corresponding clique tree (CT), necessitating approximations. Factor based methods to bound c…
Bayesian InferenceA Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System
Cutset conditioning and clique-tree propagation are two popular methods for performing exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes…
PathfinderA Note on Community Trees in Networks
We introduce the concept of community trees that summarizes topological structures within a network. A community tree is a tree structure representing clique communities from the clique percolation method (CPM). The comm…
Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks
Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging. Current algorithms require large memory space for storing GCN o…
Node ClassificationStructure-Aware Encodings of Argumentation Properties for Clique-width
Structural measures of graphs, such as treewidth, are central tools in computational complexity resulting in efficient algorithms when exploiting the parameter. It is even known that modern SAT solvers work efficiently o…