About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 55. Chapters: Geometric graphs, Graphical models, Delaunay triangulation, Bayesian network, Belief propagation, Structural equation modeling, Beta skeleton, Periodic graph, State diagram, Cayley graph, Euclidean minimum spanning tree, Steiner tree problem, Conditional random field, Circle graph, Feynman graph, Markov random field, Interval graph, Unit distance graph, Graphical models for protein structure, Collaboration graph, Hierarchical Bayes model, Factor graph, Relative neighborhood graph, Unit disk graph, Path analysis, Bratteli diagram, Circular-arc graph, Nearest neighbor graph, Dependency graph, Pitteway triangulation, Rectilinear Steiner tree, Visibility graph, Permutation graph, Matchstick graph, Spatial network, Molecular graph, Geometric spanner, Levi graph, Polyhedral graph, Constraint graph, Dependability state model, Plate notation, Erd s-Diophantine graph, Transportation network, Bratteli-Vershik diagram, Minimum-weight triangulation, Ergograph, Laman graph, Tanner graph, Urquhart graph, Planar straight-line graph, Gabriel graph, Implication graph, Collider, Moral graph, M-separation, Reeb graph, Rectilinear minimum spanning tree, Wait-for graph, Coates graph, Hanan grid, Constrained Delaunay triangulation, Ancestral graph. Excerpt: A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Formally, Bayesian networks are directed acyclic graphs whose nodes represent random variables in the Bayesian sense: they may be observable quantities, la...
