Elements of Graphical Models

[1]  A. Atay-Kayis,et al.  A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models , 2005 .

[2]  Steffen L. Lauritzen,et al.  Local computation with valuations from a commutative semigroup , 1997, Annals of Mathematics and Artificial Intelligence.

[3]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[4]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[5]  Claus Dethlefsen,et al.  deal: A Package for Learning Bayesian Networks , 2003 .

[6]  Walter Krämer,et al.  Review of Modern applied statistics with S, 4th ed. by W.N. Venables and B.D. Ripley. Springer-Verlag 2002 , 2003 .

[7]  T. Richardson Markov Properties for Acyclic Directed Mixed Graphs , 2003 .

[8]  David Maxwell Chickering,et al.  Optimal Structure Identification With Greedy Search , 2002, J. Mach. Learn. Res..

[9]  Béla Bollobás,et al.  Modern Graph Theory , 2002, Graduate Texts in Mathematics.

[10]  Steffen L. Lauritzen,et al.  Representing and Solving Decision Problems with Limited Information , 2001, Manag. Sci..

[11]  Susanne Bottcher,et al.  Learning Bayesian networks with mixed variables , 2001, AISTATS.

[12]  Ioan Todinca,et al.  Treewidth and Minimum Fill-in: Grouping the Minimal Separators , 2001, SIAM J. Comput..

[13]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[14]  David Heckerman,et al.  Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions , 1999, UAI.

[15]  Anne Berry,et al.  Generating All the Minimal Separators of a Graph , 1999, Int. J. Found. Comput. Sci..

[16]  D. Nilsson,et al.  An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems , 1998, Stat. Comput..

[17]  Dan Geiger,et al.  A Practical Algorithm for Finding Optimal Triangulations , 1997, AAAI/IAAI.

[18]  Christopher Meek,et al.  Strong completeness and faithfulness in Bayesian networks , 1995, UAI.

[19]  R. Jirousek,et al.  On the effective implementation of the iterative proportional fitting procedure , 1995 .

[20]  David Heckerman,et al.  A Characterization of the Dirichlet Distribution Through Global and Local Independence , 1994, UAI 1994.

[21]  Frank Jensen,et al.  From Influence Diagrams to junction Trees , 1994, UAI.

[22]  Frank Jensen,et al.  Optimal junction Trees , 1994, UAI.

[23]  David Heckerman,et al.  Learning Gaussian Networks , 1994, UAI.

[24]  A. Dawid,et al.  Hyper Markov Laws in the Statistical Analysis of Decomposable Graphical Models , 1993 .

[25]  Hanns-Georg Leimer,et al.  Optimal decomposition by clique separators , 1993, Discret. Math..

[26]  Søren Ladegaard Buhl On the Existence of Maximum Likelihood Estimators for Graphical Gaussian Models , 1993 .

[27]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[28]  F. Matús On equivalence of Markov properties over undirected graphs , 1992, Journal of Applied Probability.

[29]  Edward Allburn,et al.  Graph decomposition , 1990 .

[30]  David Aldous,et al.  The Random Walk Construction of Uniform Spanning Trees and Uniform Labelled Trees , 1990, SIAM J. Discret. Math..

[31]  Steffen L. Lauritzen,et al.  Independence properties of directed markov fields , 1990, Networks.

[32]  David J. Spiegelhalter,et al.  Sequential updating of conditional probabilities on directed graphical structures , 1990, Networks.

[33]  Dan Geiger,et al.  Identifying independence in bayesian networks , 1990, Networks.

[34]  Judea Pearl,et al.  Equivalence and Synthesis of Causal Models , 1990, UAI.

[35]  M. Frydenberg Marginalization and Collapsibility in Graphical Interaction Models , 1990 .

[36]  M. Frydenberg The chain graph Markov property , 1990 .

[37]  Steffen L. Lauritzen,et al.  Bayesian updating in causal probabilistic networks by local computations , 1990 .

[38]  Kristian G. Olesen,et al.  HUGIN - A Shell for Building Bayesian Belief Universes for Expert Systems , 1989, IJCAI.

[39]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[40]  Prakash P. Shenoy,et al.  Axioms for probability and belief-function proagation , 1990, UAI.

[41]  Reinhard Diestel Simplicial decompositions of graphs - Some uniqueness results , 1987, J. Comb. Theory, Ser. B.

[42]  Prakash P. Shenoy,et al.  Propagating Belief Functions with Local Computations , 1986, IEEE Expert.

[43]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[44]  Robert E. Tarjan,et al.  Decomposition by clique separators , 1985, Discret. Math..

[45]  Robert E. Tarjan,et al.  Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs , 1984, SIAM J. Comput..

[46]  T. Speed,et al.  Decomposable graphs and hypergraphs , 1984, Journal of the Australian Mathematical Society. Series A. Pure Mathematics and Statistics.

[47]  D. Edwards,et al.  Collapsibility and response variables in contingency tables , 1983 .

[48]  M. Yannakakis Computing the Minimum Fill-in is NP^Complete , 1981 .

[49]  A. Dawid Conditional Independence for Statistical Operations , 1980 .

[50]  O. Barndorff-Nielsen Information and Exponential Families in Statistical Theory , 1980 .

[51]  P. Diaconis,et al.  Conjugate Priors for Exponential Families , 1979 .

[52]  Michel Mouchart,et al.  Discussion on "Conditional independence in statistitical theory" by A.P. Dawid , 1979 .

[53]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[54]  Robert E. Tarjan,et al.  Algorithmic Aspects of Vertex Elimination on Graphs , 1976, SIAM J. Comput..

[55]  E. Thompson,et al.  The recursive derivation of likelihoods on complex pedigrees , 1976, Advances in Applied Probability.

[56]  P. Bickel,et al.  Sex Bias in Graduate Admissions: Data from Berkeley , 1975, Science.

[57]  H. Akaike A new look at the statistical model identification , 1974 .

[58]  John Cocke,et al.  Optimal decoding of linear codes for minimizing symbol error rate (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[59]  John P. Moussouris Gibbs and Markov random systems with constraints , 1974 .

[60]  Terry J. Wagner,et al.  Consistency of an estimate of tree-dependent probability distributions (Corresp.) , 1973, IEEE Trans. Inf. Theory.

[61]  Claude Berge,et al.  Graphs and Hypergraphs , 2021, Clustering.

[62]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[63]  R. Elston,et al.  A general model for the genetic analysis of pedigree data. , 1971, Human heredity.

[64]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[65]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[66]  S. Parter The Use of Linear Graphs in Gauss Elimination , 1961 .

[67]  G. Dirac On rigid circuit graphs , 1961 .

[68]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[69]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[70]  K. Wagner Über eine Eigenschaft der ebenen Komplexe , 1937 .