All Stories

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