All Stories

  1. Probabilistic graphical models for predicting post-traumatic stress disorder in US veterans
  2. Computing the decomposable entropy of belief-function graphical models
  3. On conditional belief functions in directed graphical models in the Dempster-Shafer theory
  4. Making inferences in incomplete Bayesian networks: A Dempster-Shafer belief function approach
  5. Entropy for evaluation of Dempster-Shafer belief function models
  6. Glenn Shafer — A short biography
  7. Probability and statistics: Foundations and history. Special Issue in honor of Glenn Shafer
  8. On Conditional Belief Functions in the Dempster-Shafer Theory
  9. Entropy-Based Learning of Compositional Models from Data
  10. An interval-valued utility theory for decision making with Dempster-Shafer belief functions
  11. On properties of a new decomposable entropy of Dempster-Shafer belief functions
  12. A new hybrid logistic regression-naive Bayes classifier
  13. Define expected value for Dempster-Shafer belief functions with numeric state spaces.
  14. Evidence Gathering for Hypothesis Resolution using Judicial Evidential Reasoning
  15. Evidence Gathering for Hypothesis Resolution Using Judicial Evidential Reasoning
  16. Selecting a good subset of features for classification
  17. How much uncertainty is there in a belief functions in the Dempster-Shafer theory?
  18. A definition of decomposable entropy for Dempster-Shafer belief functions.
  19. Combination and Composition in Probabilistic Models
  20. Ambiguity aversion and a decision-theoretic framework using belief functions
  21. How to convert nonlinear functions to piecewise linear ones.
  22. On computing probabilities of dismissal of 10b-5 securities class-action cases
  23. Causal compositional models in valuation-based systems with examples in specific theories
  24. Entropy of Belief Functions in the Dempster-Shafer Theory: A New Perspective
  25. Solving Bayesian networks with discrete and continuous variables with deterministic conditionals
  26. Compositional models in valuation-based systems
  27. Causal Compositional Models in Valuation-Based Systems
  28. Two issues in using mixtures of polynomials for inference in hybrid Bayesian networks
  29. A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores
  30. A Framework for Solving Hybrid Influence Diagrams Containing Deterministic Conditional Distributions
  31. Compositional Models in Valuation-Based Systems
  32. Conditioning in Decomposable Compositional Models in Valuation-Based Systems
  33. Some practical issues in inference in hybrid Bayesian networks with deterministic conditionals
  34. Extended Shenoy–Shafer architecture for inference in hybrid bayesian networks with deterministic conditionals
  35. Inference in hybrid Bayesian networks using mixtures of polynomials
  36. A review of representation issues and modeling challenges with influence diagrams
  37. A decision theory for partially consonant belief functions
  38. A Re-definition of Mixtures of Polynomials for Inference in Hybrid Bayesian Networks
  39. Modeling challenges with influence diagrams: Constructing probability and utility models
  40. Arc reversals in hybrid Bayesian networks with deterministic variables
  41. Inference in Hybrid Bayesian Networks with Deterministic Variables
  42. Decision making with hybrid influence diagrams using mixtures of truncated exponentials
  43. Using Bayesian networks for bankruptcy prediction: Some methodological issues
  44. DISCUSSION OF KYBURG'S “BELIEVING ON THE BASIS OF THE EVIDENCE”
  45. Use of Radio Frequency Identification for Targeted Advertising: A Collaborative Filtering Approach Using Bayesian Networks
  46. Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials
  47. Knowledge representation and integration for portfolio evaluation using linear belief functions
  48. Operations for inference in continuous Bayesian networks with linear deterministic variables
  49. Sequential influence diagrams: A unified asymmetry framework
  50. Inference in hybrid Bayesian networks with mixtures of truncated exponentials
  51. On the plausibility transformation method for translating belief function models to probability models
  52. Sequential valuation networks for asymmetric decision problems
  53. Decision making on the sole basis of statistical likelihood
  54. Two axiomatic approaches to decision making using possibility theory
  55. Nonlinear Deterministic Relationships in Bayesian Networks
  56. A causal mapping approach to constructing Bayesian networks
  57. Representing asymmetric decision problems using coarse valuations
  58. Multistage Monte Carlo Method for Solving Influence Diagrams Using Local Computation
  59. Application of Uncertain Reasoning to Business Decisions: An Introduction
  60. A Comparison of Bayesian and Belief Function Reasoning
  61. A Comparison of Methods for Transforming Belief Function Models to Probability Models
  62. BAYESIAN CAUSAL MAPS AS DECISION AIDS IN VENTURE CAPITAL DECISION MAKING: METHODS AND APPLICATIONS.
  63. Modeling Financial Portfolios Using Belief Functions
  64. A Bayesian network approach to making inferences in causal maps
  65. Sequential Valuation Networks: A New Graphical Technique for Asymmetric Decision Problems
  66. Valuation network representation and solution of asymmetric decision problems
  67. Computation in Valuation Algebras
  68. A Comparison of Graphical Techniques for Asymmetric Decision Problems
  69. Some improvements to the Shenoy-Shafer and Hugin architectures for computing marginals
  70. Binary join trees for computing marginals in the Shenoy-Shafer architecture
  71. A note on Kirkwood's algebraic method for decision problems
  72. Representing and Solving Asymmetric Decision Problems Using Valuation Networks
  73. A theory of coarse utility
  74. Propagating belief functions in AND-trees
  75. A comparison of graphical techniques for decision analysis
  76. Consistency in Valuation-Based Systems
  77. REPRESENTING CONDITIONAL INDEPENDENCE RELATIONS BY VALUATION NETWORKS
  78. Conditional independence in valuation-based systems
  79. Attitude Formation Models: Insights from TETRAD
  80. Valuation Networks and Conditional Independence
  81. Using possibility theory in expert systems
  82. Valuation-Based Systems for Bayesian Decision Analysis
  83. Using Dempster-Shafer's belief-function theory in expert systems
  84. Conditional Independence in Uncertainty Theories
  85. On Spohn's rule for revision of beliefs
  86. A Fusion Algorithm for Solving Bayesian Decision Problems
  87. Belief functions and belief maintenance in artificial intelligence
  88. Probability propagation
  89. Axioms for Probability and Belief-Function Propagation
  90. A valuation-based language for expert systems
  91. Propagation of Belief Functions: A Distributed Approach
  92. Propagating belief functions in qualitative Markov trees
  93. Modifiable combining functions
  94. Competitive inventory models
  95. Qualitative Markov networks
  96. Propagating Belief Functions with Local Computations
  97. Two interpretations of the difference principle in Rawls's theory of justice
  98. A solution for noncooperative games
  99. The Banzhaf power index for political games
  100. Inducing cooperation by reciprocative strategy in non-zero-sum games
  101. A dynamic solution concept for abstract games
  102. A three-person cooperative game formulation of the world oil market
  103. A two-person non-zero-sum game model of the world oil market
  104. On Committee Decision Making: A Game Theoretical Approach
  105. On coalition formation: a game-theoretical approach
  106. On coalition formation in simple games: A mathematical analysis of Caplow's and Gamson's theories
  107. On Spohn's theory of epistemic beliefs
  108. Information sets in decision theory
  109. Axioms for Probability and Belief-Function Propagation