Publications

Publications in Referenced Journals

  1. Tan, Y., B. Sherwood, and P. P. Shenoy, "A naïve Bayes regularized logistic regression estimator for low-dimensional classification," International Journal of Approximate Reasoning, 172(9), 2024, 109239. A naïve Bayes regularized logistic regression estimator for low-dimensional classification (DOI)  A naïve Bayes regularized logistic regression estimator for low-dimensional classification (PDF) (1.70 MB).
  2. Tan, Y., P. P. Shenoy, B. Sherwood, C. Shenoy, M. Gaddy, and M. E. Oehlert, "Bayesian network models for PTSD screening in veterans," INFORMS Journal on Computing, 36(2), 2024, 495--509. Bayesian network models for PTSD screening in veterans (DOI)  Bayesian network models for PTSD screening in veterans (PDF) (2.4 MB).
  3. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "Computing the decomposable entropy of belief-function graphical models," International Journal of Approximate Reasoning, 161(10), 2023, 108984. Computing the decomposable entropy of belief-function graphical models (DOI)  Computing the decomposable entropy of belief-function graphical models (PDF) (1.065 MB)
  4. Shenoy, P. P., "Making inferences in incomplete Bayesian networks: A Dempster-Shafer belief function approach," International Journal of Approximate Reasoning, 160(9), 2023, 108967. Making inferences in incomplete Bayesian networks: A Dempster-Shafer belief function approach (DOI)Making inferences in incomplete Bayesian networks: A Dempster-Shafer belief function approach (PDF) (616.6 KB)
  5. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "On conditional belief functions in directed graphical models in the Dempster-Shafer theory," International Journal of Approximate Reasoning, 160(7), 2023, 108976. On conditional belief functions in directed graphical models in the Dempster-Shafer theory (DOI)  On conditional belief functions in directed graphical models in the Dempster-Shafer theory (PDF) (643.9 KB)
  6. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "Entropy for evaluation of Dempster-Shafer belief function models," International Journal of Approximate Reasoning, 151(12), 2022, 164--181. Entropy for evaluation of Dempster-Shafer belief function models (DOI)  Entropy for evaluation of Dempster-Shafer belief function models (PDF) (256 KB)
  7. Aldrich, J. C., A. P. Dawid, T. Denœux, P. P. Shenoy, and V. Vovk, "Probability and statistics: Foundations and history," International Journal of Approximate Reasoning, 141(2), 2022, 1--4. Probability and statistics: Foundations and history (DOI)  Probability and statistics: Foundations and history (PDF) (131 KB)
  8. Aldrich, J. C., A. P. Dawid, T. Denœux, P. P. Shenoy, and V. Vovk, "Glenn Shafer---A short biography," International Journal of Approximate Reasoning, 141(2), 2022, 5--10. Glenn Shafer---A short biography (DOI)  Glenn Shafer---A short biography (PDF) (131 KB)
  9. Denœux, T. and P. P. Shenoy, "An interval-valued utility theory for decision making with Dempster-Shafer belief functions," International Journal of Approximate Reasoning, 124(9), 2020, 194--216. An interval-valued utility theory for decision making with Dempster-Shafer belief functions (DOI)  An interval-valued utility theory for decision making with Dempster-Shafer belief functions (PDF) (607 KB)
  10. Jiroušek, R. and P. P. Shenoy, "On properties of a new decomposable entropy of Dempster-Shafer belief functions," International Journal of Approximate Reasoning, 119(4), 2020, 260--279. On properties of a new decomposable entropy of Dempster-Shafer belief functions (DOI)  On properties of a new decomposable entropy of Dempster-Shafer belief functions (PDF) (765 KB)
  11. Tan, Y. and P. P. Shenoy, "A bias-variance based heuristic for constructing a hybrid logistic regression-naïve Bayes model for classification," International Journal of Approximate Reasoning, 117(2), 2020, 15--28. A bias-variance based heuristic for constructing a hybrid logistic regression-naïve Bayes model for classification (DOI)  A bias-variance based heuristic for constructing a hybrid logistic regression-naïve Bayes model for classification (PDF) (468 KB). A working paper that includes R code for implementing our algorithm is included in WP337 (538 KB).
  12. Shenoy, P. P., "An expectation operator for belief functions in the Dempster-Shafer theory," International Journal of General Systems, 49(1), 2020, 112--141. An expectation operator for belief functions in the Dempster-Shafer theory (DOI)  An expectation operator for belief functions in the Dempster-Shafer theory (International Journal of General Systems, 2020 — PDF) (460 KB).
  13. Jaunzemis, A. D., M. J. Holzinger, M. W. Chan, and P. P. Shenoy, "Evidence gathering for hypothesis resolution using judicial evidential reasoning," Information Fusion, 49(9), 2019, 26--45. Evidence gathering for hypothesis resolution using judicial evidential reasoning (DOI)  Evidence gathering for hypothesis resolution using judicial evidential reasoning (Information Fusion, 2019 — PDF)
  14. Singha, S. and P. P. Shenoy, "An adaptive heuristic for feature selection based on complementarity," Machine Learning, 107(12), 2018, 2027--2071. An adaptive heuristic for feature selection based on complementarity (DOI)  An adaptive heuristic for feature selection based on complementarity (PDF) (402KB)
  15. Jiroušek, R. and P. P. Shenoy, "A new definition of entropy of belief functions in the Dempster-Shafer theory," International Journal of Approximate Reasoning, 92(1), 2018, 49--65. A new definition of entropy of belief functions in the Dempster-Shafer theory (DOI)  A new definition of entropy of belief functions in the Dempster-Shafer theory (PDF) (709.1KB)
  16. Cobb, B. R. and P. P. Shenoy, "Inference in hybrid Bayesian networks with nonlinear deterministic conditionals," International Journal of Intelligent Systems, 32(12), 2017, 1217--1246. Inference in hybrid Bayesian networks with nonlinear deterministic conditionals (DOI)  Inference in hybrid Bayesian networks with nonlinear deterministic conditionals (PDF) (1.199MB)
  17. Singha, S., S. Hillmer, and P. P. Shenoy, "On computing probabilities of dismissal of 10b-5 securities class-action cases," Decision Support Systems, 94(2), 2017, 29--41. On computing probabilities of dismissal of 10b-5 securities class-action cases (DOI)  On computing probabilities of dismissal of 10b-5 securities class-action cases (PDF) (1.60 MB)
  18. Cinicioglu, E. N. and P. P. Shenoy, "A new heuristic for learning Bayesian networks from limited datasets: A real-time recommendation system application with RFID system in grocery stores," Annals of Operations Research, 244(2), 2016, 385--405. A new heuristic for learning Bayesian networks from limited datasets: A real-time recommendation system application with RFID system in grocery stores (DOI)  A new heuristic for learning Bayesian networks from limited datasets: A real-time recommendation system application with RFID system in grocery stores (PDF) (823 KB)
  19. Jiroušek, R. and P. P. Shenoy, "Causal compositional models in valuation-based systems with examples in specific theories," International Journal of Approximate Reasoning, 72(1), 2016, 95--112. Causal compositional models in valuation-based systems with examples in specific theories (DOI)  Causal compositional models in valuation-based systems with examples in specific theories (PDF)
  20. Shenoy, P. P., R. Rumí and A. Salmerón, "Practical aspects of solving hybrid Bayesian networks containing deterministic conditionals," International Journal of Intelligent Systems, 30(3), 2015, 265--291. Practical aspects of solving hybrid Bayesian networks containing deterministic conditionals (DOI)  Practical aspects of solving hybrid Bayesian networks containing deterministic conditionals (PDF)
  21. Jiroušek, R. and P. P. Shenoy, "Compositional models in valuation-based systems," International Journal of Approximate Reasoning, 55(1), 2014, 277--293. Compositional models in valuation-based systems (DOI)  Compositional models in valuation-based systems (PDF)
  22. Shenoy, P. P., "Two issues in using mixtures of polynomials for inference in hybrid Bayesian networks," International Journal of Approximate Reasoning, 53(5), 2012, 847--866. Two issues in using mixtures of polynomials for inference in hybrid Bayesian networks (DOI)  Two issues in using mixtures of polynomials for inference in hybrid Bayesian networks (PDF) (1.4 MB). A copy of this paper that includes Mathematica code for all results in the paper can be downloaded as WP No. 323 (10.2 MB).
  23. Li, Y. and P. P. Shenoy, "A framework for solving hybrid influence diagrams containing deterministic conditional distributions," Decision Analysis, 9(1), 2012, 55--75. A framework for solving hybrid influence diagrams containing deterministic conditional distributions (DOI)A framework for solving hybrid influence diagrams containing deterministic conditional distributions (PDF) (1.7MB). A copy of this paper that includes Mathematica code for the solution of the two examples in the paper can be downloaded as WP No. 322 (8.1 MB).
  24. Shenoy, P. P. and J. C. West, "Extended Shenoy-Shafer architecture for inference in hybrid Bayesian networks with deterministic conditionals," International Journal of Approximate Reasoning, 52(6)0, 2011, 805--818. Extended Shenoy-Shafer architecture for inference in hybrid Bayesian networks with deterministic conditionals (DOI)  Extended Shenoy-Shafer architecture for inference in hybrid Bayesian networks with deterministic conditionals (PDF) (1 MB).
  25. Shenoy, P. P. and J. C. West, "Inference in hybrid Bayesian networks using mixtures of polynomials," International Journal of Approximate Reasoning, 52(5), 2011, 641--657. Inference in hybrid Bayesian networks using mixtures of polynomials (DOI)  Inference in hybrid Bayesian networks using mixtures of polynomials (PDF) (766 KB).
  26. Giang, P. H. and P. P. Shenoy, "A decision theory for partially consonant belief functions," International Journal of Approximate Reasoning, 52(3), 2011, 375--394. A decision theory for partially consonant belief functions (DOI)  A decision theory for partially consonant belief functions (PDF) (564 KB).
  27. Bielza, C., M. Gomez, and P. P. Shenoy, "A review of representation issues and modeling challenges with influence diagrams," Omega: International Journal of Management Science, 39(3), 2011, 227--241. A review of representation issues and modeling challenges with influence diagrams (DOI)  A review of representation issues and modeling challenges with influence diagrams (PDF) (311 KB).
  28. Bielza, C., M. Gomez, and P. P. Shenoy, "Modeling challenges with influence diagrams: Constructing probability and utility models," Decision Support Systems, 49(4), 2010, 354--364. Modeling challenges with influence diagrams: Constructing probability and utility models (DOI)  Modeling challenges with influence diagrams: Constructing probability and utility models (PDF) (471 KB).
  29. Cinicioglu, E. N. and P. P. Shenoy, "Arc reversals in hybrid Bayesian networks with deterministic variables," International Journal of Approximate Reasoning, 50(5), 2009, 763--777. Arc reversals in hybrid Bayesian networks with deterministic variables (DOI)  Arc reversals in hybrid Bayesian networks with deterministic variables (PDF) (1.1 MB).
  30. Cobb, B. R. and P. P. Shenoy, "Decision making with hybrid influence diagrams using mixtures of truncated exponentials," European Journal of Operational Research, 186(1), 2008, 261--275. Decision making with hybrid influence diagrams using mixtures of truncated exponentials (DOI)  Decision making with hybrid influence diagrams using mixtures of truncated exponentials (PDF) (216 KB).
  31. Sun, L. and P. P. Shenoy, "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, 180(2), 2007, 738--753. Using Bayesian networks for bankruptcy prediction: Some methodological issues (DOI)  Using Bayesian networks for bankruptcy prediction: Some methodological issues (PDF) (371 KB).
  32. Liu, L., C. Shenoy, and P. P. Shenoy, "Knowledge representation and integration for portfolio evaluation using linear belief functions," IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 36(4), 2006, 774--785. Knowledge representation and integration for portfolio evaluation using linear belief functions (DOI)  Knowledge representation and integration for portfolio evaluation using linear belief functions (PDF) (5.2 MB).
  33. Cobb, B. R., P. P. Shenoy, and R. Rumí "Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials," Statistics and Computing, 16(3), 2006, 293--308. Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials (DOI)  Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials (PDF) (441KB).
  34. Jensen, F. V., T. D. Nielsen, and P. P. Shenoy. "Sequential influence diagrams: A unified asymmetry framework," International Journal of Approximate Reasoning, 42(1--2), 2006, 101--118. Sequential influence diagrams: A unified asymmetry framework (DOI)  Sequential influence diagrams: A unified asymmetry framework (International Journal of Approximate Reasoning, 2006 — PDF) (306 KB).
  35. Cobb, B. R. and P. P. Shenoy, "Operations for inference in continuous Bayesian networks with linear deterministic variables," International Journal of Approximate Reasoning, 42(1--2), 2006, 21--36. Operations for inference in continuous Bayesian networks with linear deterministic variables (DOI)  Operations for inference in continuous Bayesian networks with linear deterministic variables (PDF) (323 KB).
  36. Cobb, B. R. and P. P. Shenoy, "On the plausibility transformation method for translating belief function models to probability models," International Journal of Approximate Reasoning, 41(3), 2006, 314--340. On the plausibility transformation method for translating belief function models to probability models (DOI)  On the plausibility transformation method for translating belief function models to probability models (PDF) (785KB). A longer working paper with more details and examples can be downloaded as Working Paper No. 293 (1440 KB)
  37. Cobb, B. R. and P. P. Shenoy, "Inference in hybrid Bayesian networks with mixtures of truncated exponentials," International Journal of Approximate Reasoning, 41(3), 2006, 257--286. Inference in hybrid Bayesian networks with mixtures of truncated exponentials (DOI)  Inference in hybrid Bayesian networks with mixtures of truncated exponentials (International Journal of Approximate Reasoning, 2006 — PDF) (556 KB).
  38. Demirer, R. and P. P. Shenoy, "Sequential valuation networks for asymmetric decision problems," European Journal of Operational Research, 169(1), 2006, 286--309. Sequential valuation networks for asymmetric decision problems (DOI)  Sequential valuation networks for asymmetric decision problems (PDF) (756 KB).
  39. Giang, P. H. and P. P. Shenoy, "Decision making on the sole basis of statistical likelihood," Artificial Intelligence, 165(2), 2005, 137--163. Decision making on the sole basis of statistical likelihood (DOI)  Decision making on the sole basis of statistical likelihood (PDF) (500 KB)
  40. Giang, P. H. and P. P. Shenoy, "Two axiomatic approaches to decision making using possibility theory," European Journal of Operational Research, 162(2), 2005, 450--467. Two axiomatic approaches to decision making using possibility theory (DOI)  Two axiomatic approaches to decision making using possibility theory (PDF) (306 KB).
  41. Nadkarni, S. and P. P. Shenoy, "A causal mapping approach to constructing Bayesian networks," Decision Support Systems, 38(2), 2004, 259--281. A causal mapping approach to constructing Bayesian networks (DOI)  A causal mapping approach to constructing Bayesian networks (PDF) (304 KB).
  42. Liu, L. and P. P. Shenoy, "Representing asymmetric decision problems using coarse valuations" Decision Support Systems, 37(1), 2004, 119--135. Representing asymmetric decision problems using coarse valuations (DOI)  Representing asymmetric decision problems using coarse valuations (PDF) (274 KB).
  43. Charnes, J. M. and P. P. Shenoy, "Multi-stage Monte Carlo method for solving influence diagrams using local computation," Management Science, 50(3), 2004, 405--418. Multi-stage Monte Carlo method for solving influence diagrams using local computation (DOI)  Multi-stage Monte Carlo method for solving influence diagrams using local computation (PDF) (188 KB). An online appendix to the published version is also available at the same URL.
  44. Cobb, B. R. and P. P. Shenoy, "A comparison of Bayesian and belief function reasoning," Information Systems Frontiers, 5(4), 2003, 345--358. A comparison of Bayesian and belief function reasoning (DOI)  A comparison of Bayesian and belief function reasoning (PDF) (404 KB).
  45. Nadkarni, S. and P. P. Shenoy, "A Bayesian network approach to making inferences in causal maps," European Journal of Operational Research, 128(3), 2001, 479--498. A Bayesian network approach to making inferences in causal maps (DOI)  A Bayesian network approach to making inferences in causal maps (PDF) (344 KB).
  46. Shenoy, P. P., "Valuation network representation and solution of asymmetric decision problems," European Journal of Operational Research, 121(3), 2000, 579--608. Valuation network representation and solution of asymmetric decision problems (DOI)  Valuation network representation and solution of asymmetric decision problems (PDF) (472 KB).
  47. Bielza, C. and P. P. Shenoy, "A comparison of graphical techniques for asymmetric decision problems," Management Science, 45(11), 1999, 1552--1569. A comparison of graphical techniques for asymmetric decision problems (DOI)  A comparison of graphical techniques for asymmetric decision problems (PDF) (316K). A supplement to the published version is available (570 KB).
  48. Schmidt, T. and P. P. Shenoy, "Some improvements to the Shenoy-Shafer and Hugin architectures for computing marginals," Artificial Intelligence, 102(2), 1998, 323--333. Some improvements to the Shenoy-Shafer and Hugin architectures for computing marginals (DOI)  Some improvements to the Shenoy-Shafer and Hugin architectures for computing marginals (PDF) (129 KB).
  49. Shenoy, P. P., "Game trees for decision analysis," Theory and Decision, 44(2), 1998, 149--171. Game trees for decision analysis (DOI)  Game trees for decision analysis (PDF) (150 KB).
  50. Shenoy, P. P., "Binary join trees for computing marginals in the Shenoy-Shafer architecture," International Journal of Approximate Reasoning, 17(2--3), 1997, 239--263. Binary join trees for computing marginals in the Shenoy-Shafer architecture (DOI)  Binary join trees for computing marginals in the Shenoy-Shafer architecture (PDF) (335 KB).
  51. Guo, R. and P. P. Shenoy, "A note on Kirkwood's algebraic method for decision problems," European Journal of Operational Research, 93(3), 1996, 628--638. A note on Kirkwood's algebraic method for decision problems (DOI)  A note on Kirkwood's algebraic method for decision problems (PDF) (133 KB).
  52. Srivastava, R. P., P. P. Shenoy, and G. Shafer, "Propagating belief functions in and-trees," International Journal of Intelligent Systems, 10(7), 1995, 647--664. Propagating belief functions in and-trees (DOI)  Propagating belief functions in and-trees (PDF)
  53. Liu, L. and P. P. Shenoy, "A theory of coarse utility," Journal of Risk and Uncertainty, 11, 1995, 17--49. A theory of coarse utility (DOI)  A theory of coarse utility (PDF)
  54. Shenoy, P. P., "Consistency in valuation-based systems," ORSA Journal on Computing, 6(3), 1994, 281--291. Consistency in valuation-based systems (DOI)  Consistency in valuation-based systems (PDF)
  55. Shenoy, P. P., "A comparison of graphical techniques for decision analysis," European Journal of Operational Research, 78(1), 1994, 1--21. A comparison of graphical techniques for decision analysis (DOI)  A comparison of graphical techniques for decision analysis (PDF) (630 KB).
  56. Shenoy, P. P., "Representing conditional independence relations by valuation networks," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2(2), 1994, 143--165. Representing conditional independence relations by valuation networks (DOI)  Representing conditional independence relations by valuation networks (PDF) (3.1 MB)
  57. Shenoy, P. P., "Conditional independence in valuation-based systems," International Journal of Approximate Reasoning, 10(3), 1994, 203--234. Conditional independence in valuation-based systems (DOI)  Conditional independence in valuation-based systems (PDF) (641 KB).
  58. Shenoy, P. P., "Using possibility theory in expert systems," Fuzzy Sets and Systems, 52(2), 1992, 129--142. Using possibility theory in expert systems (DOI)  Using possibility theory in expert systems (PDF) (158 KB).
  59. Shenoy, P. P., "Valuation-Based Systems for Bayesian Decision Analysis," Operations Research, Vol. 40, No. 3, 1992, pp. 463--484. Valuation-Based Systems for Bayesian Decision Analysis (DOI)  Valuation-Based Systems for Bayesian Decision Analysis (PDF) (3579 KB)
  60. Shenoy, P. P., "On Spohn's rule for revision of beliefs," International Journal of Approximate Reasoning, 5(2), 1991, 149--181. On Spohn's rule for revision of beliefs (DOI)  On Spohn's rule for revision of beliefs (PDF) (1.4MB)
  61. Shafer, G. R. and P. P. Shenoy, "Probability propagation," Annals of Mathematics and Artificial Intelligence, 2(1--4), 1990, 327--352. Probability propagation (DOI)  Probability propagation (PDF) (244 KB)
  62. Shenoy, P. P., "A valuation-based language for expert systems," International Journal of Approximate Reasoning, 3(2), 1989, 383--411. A valuation-based language for expert systems (DOI)  A valuation-based language for expert systems (PDF) (1.3MB)
  63. Cohen, P., G. Shafer, and P. P. Shenoy, "Modifiable combining functions," Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 1(1), 1987, 47--57. Modifiable combining functions (DOI)  Modifiable combining functions (PDF)
  64. Shafer, G., P. P. Shenoy, and K. Mellouli, "Propagating belief functions in qualitative Markov trees," International Journal of Approximate Reasoning, 1(4), 1987, 349--400. Propagating belief functions in qualitative Markov trees (DOI)  Propagating belief functions in qualitative Markov trees (PDF) (2.7MB)
  65. Shenoy, P. P., "Competitive inventory models," RAIRO-Operations Research, 21(1), 1987, 1--19. Competitive inventory models (DOI)  Competitive inventory models (PDF) (1.4 MB)
  66. Shenoy, P. P. and G. Shafer, "Propagating belief functions with local computations," IEEE Expert, 1(3), 1986, 43--52. Propagating belief functions with local computations (DOI)  Propagating belief functions with local computations (PDF) (8.9MB)
  67. Shenoy, P. P. and R. Martin, "Two interpretations of the difference principle in Rawls' theory of justice," Theoria, 49(3), 1983, 113--141. Two interpretations of the difference principle in Rawls' theory of justice (DOI)  Two interpretations of the difference principle in Rawls' theory of justice (PDF)
  68. Shenoy, P. P., "The Banzhaf power index for political games," Mathematical Social Sciences, 2(3), 1982, 299--315. The Banzhaf power index for political games (DOI)  The Banzhaf power index for political games (PDF) (1.3MB)
  69. Shenoy, P. P., "A solution for noncooperative games," Journal of Optimization Theory and Applications, 38(4), 1982, 565--579. A solution for noncooperative games (DOI)
  70. Shenoy, P. P., and P.-L. Yu, "Inducing cooperation by reciprocative strategy in non-zero-sum games," Journal of Mathematical Analysis and Applications, 80(1), 1981, 67--77. Inducing cooperation by reciprocative strategy in non-zero-sum games (DOI)  Inducing cooperation by reciprocative strategy in non-zero-sum games (PDF)
  71. Shenoy, P. P., "A three-person cooperative game model of the world oil market," Applied Mathematical Modeling, 4(4), 1980, 301--307. A three-person cooperative game model of the world oil market (DOI)  A three-person cooperative game model of the world oil market (PDF)
  72. Shenoy, P. P., "A two-person non-zero-sum game model of the world oil market," Applied Mathematical Modeling, 4(4), 1980, 295--300. A two-person non-zero-sum game model of the world oil market (DOI)  A two-person non-zero-sum game model of the world oil market (PDF)
  73. Shenoy, P. P., "A dynamic solution concept for abstract games," Journal of Optimization Theory and Applications, 32(2), 1980, 151--169. A dynamic solution concept for abstract games (DOI)
  74. Shenoy, P. P., "On committee decision making: A game-theoretical approach," Management Science, 26(4), 1980, 387--400. On committee decision making: A game-theoretical approach (DOI)  On committee decision making: A game-theoretical approach (PDF) (1336 KB)
  75. Shenoy, P. P., "On coalition formation: A game-theoretical approach," International Journal of Game Theory, 8(3), 1979, 133--164. On coalition formation: A game-theoretical approach (DOI)  On coalition formation: A game-theoretical approach (PDF) (1.32 MB)
  76. Shenoy, P. P., "On coalition formation in simple games: A mathematical analysis of Caplow's and Gamson's theories," Journal of Mathematical Psychology, 18(2), 1978, 177--194. On coalition formation in simple games: A mathematical analysis of Caplow's and Gamson's theories (DOI)  On coalition formation in simple games: A mathematical analysis of Caplow's and Gamson's theories (PDF) (883KB)

Publications in Refereed Edited Books

  1. Shenoy, P. P., "Mutual information and Kullback-Leibler divergence in the Dempster-Shafer theory," in Y. Bi, A.-L. Jousselme, and T. Denœux (eds.), Belief Functions: Theory and Applications: 8th International Conference, BELIEF-2024, Lecture Notes in Artificial Intelligence, Vol. 14909, 2024, 225--233, Springer Nature, Cham. Mutual information and Kullback-Leibler divergence in the Dempster-Shafer theory (DOI)Mutual information and Kullback-Leibler divergence in the Dempster-Shafer theory (PDF) (350 KB)
  2. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "On the relationship between graphical and compositional models for the Dempster-Shafer theory of belief functions," in E. Miranda, I. Montes, E. Quaeghebeur, and B. Vantaggi (eds.), Proceedings of the 13th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA-23), Proceedings of Machine Learning Research (PMLR), Vol. 215, 259--269, 2023, MLR Press. On the relationship between graphical and compositional models for the Dempster-Shafer theory of belief functions (Web)On the relationship between graphical and compositional models for the Dempster-Shafer theory of belief functions (PDF) (388 KB)
  3. Shenoy, P. P., "On distinct belief functions in the Dempster-Shafer theory," in E. Miranda, I. Montes, E. Quaeghebeur, and B. Vantaggi (eds.), Proceedings of the 13th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA-23), Proceedings of Machine Learning Research (PMLR), Vol. 215, 426--437, 2023, MLR Press. On distinct belief functions in the Dempster-Shafer theory (Web)On distinct belief functions in the Dempster-Shafer theory (ISIPTA-23, 2023 — PDF) (515 KB)
  4. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "On conditional belief functions in the Dempster-Shafer theory," in S. Le Hégarat-Mascle, I. Bloch, and E. Aldea (eds.), Belief Functions: Theory and Applications, 7th International Conference, BELIEF 2022, Lecture Notes in Artificial Intelligence, Vol. 13506, 207--218, 2022, Springer Nature, Switzerland. On conditional belief functions in the Dempster-Shafer theory (DOI)On conditional belief functions in the Dempster-Shafer theory (PDF) (356 KB)
  5. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "Entropy-based learning of compositional models from data," in T. Denœux, E. Lefèvre, Z. Liu, and F. Pichon (eds.), Belief Functions: Theory and Applications, Proceedings of the 6th International Conference, BELIEF 2021, Lecture Notes in Artificial Intelligence, Vol. 12915, 117--126, 2021, Springer Nature, Switzerland. Entropy-based learning of compositional models from data (DOI)Entropy-based learning of compositional models from data (PDF) (353 KB)
  6. Denœux, T. and P. P. Shenoy, "An axiomatic utility theory for Dempster-Shafer belief functions," in J. de Bock, C. P. de Campos, G. de Cooman, E. Quaeghebeur, and G. Wheeler (eds.), Proceedings of the 11th International Symposium on Imprecise Probabilities: Theories and Applications, Proceedings of Machine Learning Research (PMLR), Vol. 103, 2019, 145--155. An axiomatic utility theory for Dempster-Shafer belief functions (Web)An axiomatic utility theory for Dempster-Shafer belief functions (PDF)
  7. Jiroušek, R. and P. P. Shenoy, "A decomposable entropy of belief functions in the Dempster-Shafer theory," in S. Destercke, T. Denœux, F. Cuzzolin, and A. Martin (eds.), Belief Functions: Theory and Applications, 5th International Conference, BELIEF 2018, Lecture Notes in Artificial Intelligence, Vol. 11069, 146--154, 2018, Springer Nature, Switzerland. A decomposable entropy of belief functions in the Dempster-Shafer theory (DOI)A decomposable entropy of belief functions in the Dempster-Shafer theory (PDF) (946 KB)
  8. Jiroušek, R. and P. P. Shenoy, "Combination and composition in probabilistic models," in L. H. Ahn, L. S. Dong, V. Kreinovich, and N. N. Thach (eds.), Econometrics for Financial Applications: ECONVN 2018 Conference Proceedings, Studies in Computational Intelligence, Vol. 760, 2018, 120--133, Springer, Cham. Combination and composition in probabilistic models (DOI)Combination and composition in probabilistic models (PDF) (172 KB)
  9. Tan, Y., P. P. Shenoy, M. W. Chan, and P. M. Romberg, "On construction of hybrid logistic regression-naïve Bayes model for classification," in A. Antonucci, G. Corani, and C. P. de Campos (eds.), Proceedings of Machine Learning Research, Vol. 52: Conference on Probabilistic Graphical Models, 2016, 523--534, Lugano, Switzerland, MLR Press. On construction of hybrid logistic regression-naïve Bayes model for classification (PDF) (197 KB)

Publications in Refereed Conference Proceedings

  1. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "Two composition operators for belief functions revisited," in M. Studený, N. Ay, G. Coletti, G. D. Kleiter, and P. P. Shenoy (eds.), Proceedings of the 12th Workshop on Uncertainty Processing (WUPES'22), 123--134, 2022, MatfyzPress, Prague, Czechia. Two composition operators for belief functions revisited (PDF) (356 KB)
  2. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "Computing the decomposable entropy of graphical belief function models," in M. Studený, N. Ay, G. Coletti, G. D. Kleiter, and P. P. Shenoy (eds.), Proceedings of the 12th Workshop on Uncertainty Processing (WUPES'22), 111--122, 2022, MatfyzPress, Prague, Czechia. Computing the decomposable entropy of graphical belief function models (PDF) (637 KB)
  3. Marsillach, D. A., S. Virani, M. J. Holzinger, M. W. Chan, and P. P. Shenoy, "Real-time telescope tasking for custody and anomaly resolution using judicial evidential reasoning," in Proceedings of 29th AAS/AIAA Space Flight Mechanics Meeting, AAS-534, 2019. Real-time telescope tasking for custody and anomaly resolution using judicial evidential reasoning (PDF)
  4. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "Two composition operators for belief functions revisited," in M. Studený, N. Ay, G. Coletti, G. D. Kleiter, and P. P. Shenoy (eds.), Proceedings of the 12th Workshop on Uncertainty Processing (WUPES'22), 123--134, 2022, MatfyzPress, Prague, Czechia. Two composition operators for belief functions revisited (PDF) (356 KB)
  5. Jiroušek, R., V. Kratochvíl, and P. P. Shenoy, "Computing the decomposable entropy of graphical belief function models," in M. Studený, N. Ay, G. Coletti, G. D. Kleiter, and P. P. Shenoy (eds.), Proceedings of the 12th Workshop on Uncertainty Processing (WUPES'22), 111--122, 2022, MatfyzPress, Prague, Czechia. Computing the decomposable entropy of graphical belief function models (PDF) (637 KB)
  6. Marsillach, D. A., S. Virani, M. J. Holzinger, M. W. Chan, and P. P. Shenoy, "Real-time telescope tasking for custody and anomaly resolution using judicial evidential reasoning," in Proceedings of 29th AAS/AIAA Space Flight Mechanics Meeting, AAS-534, 2019. Real-time telescope tasking for custody and anomaly resolution using judicial evidential reasoning (PDF)
  7. Jaunzemis, A. D., M. J. Holzinger, M. W. Chan, and P. P. Shenoy, "Evidence gathering for hypothesis resolution using judicial evidential reasoning," in Fusion-2018: Proceedings of the 21st International Conference on Information Fusion, 2626--2633, IEEE, Piscataway, NJ. Evidence gathering for hypothesis resolution using judicial evidential reasoning (Fusion-2018 proceedings, 2018 — PDF) (380 KB)
  8. Shenoy, P. P., "An expectation operator for belief functions in the Dempster-Shafer theory," in V. Kratochvíl and J. Vejnarová (eds.), Proceedings of the 11th Workshop on Uncertainty Processing, 2018, 165--176, MatfyzPress, Praha, Czech Republic. An expectation operator for belief functions in the Dempster-Shafer theory (WUPES-18 proceedings, 2018 — PDF) (303 KB)
  9. Jiroušek, R. and P. P. Shenoy, "Ambiguity aversion and a decision-theoretic framework using belief functions," in 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 2017, 326--332, IEEE, Piscataway, NJ. Ambiguity aversion and a decision-theoretic framework using belief functions (PDF) (1.76 MB)
  10. Jaunzemis, A. D., D. Minotra, M. J. Holzinger, K. M. Feigh, M. W. Chan, and P. P. Shenoy, "Judicial evidential reasoning for decision support applied to orbit insertion failure," in First International Academy of Astronautics (IIA) Conference on Space Situational Awareness, 2017. Judicial evidential reasoning for decision support applied to orbit insertion failure (PDF) (946 KB)
  11. Cobb, B. R. and P. P. Shenoy, "Piecewise linear approximations of nonlinear deterministic conditionals in continuous Bayesian networks," in A. Cano, M. Gómez-Olmedo, and T. D. Nielsen (eds.), Proceedings of the 6th European Workshop on Probabilistic Graphical Models (PGM-12), 2012, 59--66, DECSAI University of Granada, Spain. Piecewise linear approximations of nonlinear deterministic conditionals in continuous Bayesian networks (PDF) (245 KB)
  12. Rumí, R., A. Salmerón, and P. P. Shenoy, "Tractable inference in hybrid Bayesian networks with deterministic conditionals using re-approximations," in A. Cano, M. Gómez-Olmedo, and T. D. Nielsen (eds.), Proceedings of the 6th European Workshop on Probabilistic Graphical Models (PGM-12), 2012, 275--282, DECSAI University of Granada, Spain. Tractable inference in hybrid Bayesian networks with deterministic conditionals using re-approximations (PDF) (540 KB)
  13. Shenoy, P. P., R. Rumí, and A. Salmerón, "Some practical issues in inference in hybrid Bayesian networks with deterministic conditionals," in S. Ventura, A. Abraham, K. Cios, C. Romero, F. Marcelloni, J. M. Benitez, and E. Gibaja (eds.), Proceedings of the 2011 Eleventh International Conference on Intelligent Systems Design and Applications (ISDA-11), 2011, 605--610, IEEE Research Publishing Services, Piscataway, NJ. Some practical issues in inference in hybrid Bayesian networks with deterministic conditionals (DOI)Some practical issues in inference in hybrid Bayesian networks with deterministic conditionals (PDF) (999 KB)
  14. Jiroušek, R. and P. P. Shenoy, "A note on factorization of belief functions," in R. Bartak (ed.), Proceedings of the Fourteenth Czech-Japan Seminar on Data Analysis and Decision Making Under Uncertainty (CJS-11), 43--51, 2011, Matfyz Press, Charles University in Prague, CZ. A note on factorization of belief functions (PDF) (319 KB)
  15. Cinicioglu, E. N. and P. P. Shenoy, "Using mixtures of truncated exponentials for solving stochastic PERT networks," in J. Vejnarova and T. Kroupa (eds.), Proceedings of the Eighth Workshop on Uncertainty Processing (WUPES-09), 269--283, 2009, University of Economics, Prague. Using mixtures of truncated exponentials for solving stochastic PERT networks (PDF) (209 KB)
  16. Shenoy, P. P. and J. C. West, "Mixtures of polynomials in hybrid Bayesian networks with deterministic variables," in J. Vejnarova and T. Kroupa (eds.), Proceedings of the Eighth Workshop on Uncertainty Processing (WUPES-09), 202--212, 2009, University of Economics, Prague. Mixtures of polynomials in hybrid Bayesian networks with deterministic variables (PDF) (651 KB)
  17. Cinicioglu, E. N. and P. P. Shenoy, "Solving stochastic PERT networks exactly using hybrid Bayesian networks," in J. Vejnarova and T. Kroupa (eds.), Proceedings of the Seventh Workshop on Uncertainty Processing (WUPES-06), 183--197, 2006, Mikulov, Czech Republic, Oeconomica Publishers. Solving stochastic PERT networks exactly using hybrid Bayesian networks (PDF) (1.12 MB)
  18. Cinicioglu, E. N. and P. P. Shenoy, "On Walley's combination rule for statistical evidence," Proceedings of the Eleventh International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-06), 386--394, 2006, Les Cordeliers, Paris, France. On Walley's combination rule for statistical evidence (PDF) (567 KB)
  19. Shenoy, P. P., "No double counting semantics for conditional independence," in F. G. Cozman, R. Nau, and T. Seidenfeld (eds.), Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications (ISIPTA-05), 2005, 306--314, Society for Imprecise Probabilities and Their Applications. No double counting semantics for conditional independence (PDF) (1.01 MB)
  20. Jensen, F. V., T. D. Nielsen, and P. P. Shenoy, "Sequential influence diagrams: A unified asymmetry framework," in P. Lucas (ed.), Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-04), 121--128, 2004, Leiden, Netherlands. Sequential influence diagrams: A unified asymmetry framework (PGM-04 proceedings, 2004 — PDF) (186 KB)
  21. Cobb, B. R. and P. P. Shenoy, "Inference in hybrid Bayesian networks with deterministic variables," in P. Lucas (ed.), Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-04), 57--64, 2004, Leiden, Netherlands. Inference in hybrid Bayesian networks with deterministic variables (PDF) (203 KB)
  22. Cobb, B. R., P. P. Shenoy, and R. Rumí, "Approximating probability density functions with mixtures of truncated exponentials," Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-04), 429--436, 2004, Perugia, Italy. Approximating probability density functions with mixtures of truncated exponentials (PDF) (376 KB)
  23. Shenoy, P. P. and R. P. Srivastava, "Application of uncertain reasoning to business decisions: An introduction," Information Systems Frontiers, Vol. 5, No. 4, 2003, 343--344. Application of uncertain reasoning to business decisions: An introduction (DOI)Application of uncertain reasoning to business decisions: An introduction (PDF)
  24. Cobb, B. R. and P. P. Shenoy, "Inference in hybrid Bayesian networks with mixtures of truncated exponentials," in J. Vejnarova (ed.), Proceedings of the Sixth Workshop on Uncertainty Processing (WUPES-03), 47--63, 2003, Hejnice, Czech Republic, VSE-Oeconomica Publishers. Inference in hybrid Bayesian networks with mixtures of truncated exponentials (WUPES-03 proceedings, 2003 — PDF) (284 KB)
  25. Mishra, S., B. Kemmerer, and P. P. Shenoy, "Managing venture capital investment decisions: A knowledge-based approach," Poster presentation at the 2001 Babson College–Kauffman Foundation Entrepreneurship Research Conference, Jönköping, Sweden, 2001. Managing venture capital investment decisions: A knowledge-based approach (PDF) (115 KB)
  26. Kemmerer, B., S. Mishra, and P. P. Shenoy, "Bayesian causal maps as decision aids in venture capital decision making: Methods and applications," in Academy of Management Proceedings, Vol. 2002, No. 1, C1--C6. Bayesian causal maps as decision aids in venture capital decision making: Methods and applications (DOI)Bayesian causal maps as decision aids in venture capital decision making: Methods and applications (Academy of Management Proceedings, 2002 — PDF) (88 KB)
  27. Liu, L. and P. P. Shenoy, "Conditional belief functions," Proceedings of the Decision Sciences Institute 1998 Annual Meeting, 589--591, Las Vegas, NV. Conditional belief functions (PDF) (492 KB)
  28. Charnes, J. M. and P. P. Shenoy, "A forward Monte Carlo method for solving influence diagrams using local computation," Preliminary Papers of the Sixth International Workshop on Artificial Intelligence and Statistics, 75--82, January 1997, Ft. Lauderdale, FL.
  29. Bielza, C. and P. P. Shenoy, "A comparison of decision trees, influence diagrams and valuation networks for asymmetric decision problems," Preliminary Papers of the Sixth International Workshop on Artificial Intelligence and Statistics, 39--48, January 1997, Ft. Lauderdale, FL.
  30. Liu, L. and P. P. Shenoy, "A decomposition method for asymmetric decision problems," Proceedings of the Decision Sciences Institute 1995 Annual Meeting, Vol. 2, 589--591, November 1995, Boston, MA.
  31. Shenoy, P. P., "Representing and solving asymmetric decision problems using valuation networks," Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and Statistics, 488--494, January 1995, Ft. Lauderdale, FL. Representing and solving asymmetric decision problems using valuation networks (PDF) (937 KB)
  32. Shenoy, P. P., "A new pruning method for solving decision trees and game trees," Proceedings of the Third Workshop on Uncertainty Processing in Expert Systems, 227--242, September 1994, Třešť, Czech Republic.
  33. Shenoy, P. P., "A discussion of Kyburg's 'Believing on the basis of the evidence'," Computational Intelligence, Vol. 10, No. 1, 92--93, 1994. A discussion of Kyburg's 'Believing on the basis of the evidence' (DOI)A discussion of Kyburg's 'Believing on the basis of the evidence' (PDF) (130 KB)
  34. Shenoy, P. P., "Valuation networks and asymmetric decision problems," Proceedings of the Fifth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Vol. 1, 1994, 153--158, Paris, France.
  35. Mishra, S. and P. P. Shenoy, "Searching for alternative representation of data: A case for TETRAD," in Preliminary Papers of the Fourth International Workshop on Artificial Intelligence and Statistics, 375--380, January 1993, Fort Lauderdale, FL.
  36. Shenoy, P. P., "Valuation networks: A new graphical representation and solution technique for decision problems," in Knowledge-Based Construction of Probabilistic and Decision Models, Workshop Notes from the Ninth National Conference on Artificial Intelligence (AAAI-91), 118--122, July 1991, Anaheim, CA.
  37. Shenoy, P. P. and G. Biswas, "Belief revision and belief maintenance in artificial intelligence: Guest editors' introduction," International Journal of Approximate Reasoning, Vol. 4, No. 5--6, 1990, 319--322. Belief revision and belief maintenance in artificial intelligence: Guest editors' introduction (DOI)Belief revision and belief maintenance in artificial intelligence: Guest editors' introduction (PDF) (179 KB)
  38. Shenoy, P. P. and R. P. Srivastava, "A graphical system for audit planning and evidence aggregation," Proceedings of the Fifth Annual Conference on Making Statistics More Effective in Schools of Business, Lawrence, KS, June 1990, 92--125.
  39. Shenoy, P. P. and G. Shafer, "Constraint propagation," Proceedings of the IJCAI-89 Workshop on Constraint Processing, Detroit, MI, July 1989, 160--163.
  40. Hsia, Y. and P. P. Shenoy, "MacEvidence: A visual environment for constructing and evaluating evidential systems," Proceedings of the World Conference on Information Processing and Communication (WOCON-INFOR 89), Seoul, South Korea, June 1989, 20--25.
  41. Shafer, G. and P. P. Shenoy, "A discussion of paper by Lauritzen and Spiegelhalter," Journal of the Royal Statistical Society, Vol. 50, Series B, 1988, p. 214. A discussion of paper by Lauritzen and Spiegelhalter (PDF) (4.5 MB)
  42. Shenoy, P. P., "A solution for non-cooperative games," Transactions of the Twenty-Fourth Conference of Army Mathematicians, Report No. 79-1, U.S. Army Research Office, 53--66, January 1979. A solution for non-cooperative games (PDF) (400 KB)

Unpublished Working Papers

  1. Shenoy, P. P., and V. Kratochvíl, "Mutual information and Kullback-Leibler divergence in the Dempster-Shafer theory of belief functions," Working Paper No. 345, July 2025, School of Business, University of Kansas. Mutual information and Kullback-Leibler divergence in the Dempster-Shafer theory of belief functions (PDF) (506 KB)
  2. Shenoy, P. P., "On distinct belief functions in the Dempster-Shafer theory," Working Paper No. 344, February 2023, revised July 2025, School of Business, University of Kansas. On distinct belief functions in the Dempster-Shafer theory (Working Paper No. 344, 2025 — PDF) (649 KB)
  3. Hillmer, S., and P. P. Shenoy, "A model for estimating Medicare/Supplemental Security Income fraction for 340B program qualification," Working Paper No. 331, August 2014, revised January 2016, School of Business, University of Kansas. A model for estimating Medicare/Supplemental Security Income fraction for 340B program qualification (PDF) (275 KB)
  4. Shenoy, P. P., "Representing piecewise functions in Mathematica(c)," Working Paper No. 324, March 2011, School of Business, University of Kansas. Representing piecewise functions in Mathematica(c) (PDF) (225 KB)
  5. Kemmerer, B., S. Mishra, and P. P. Shenoy, "Bayesian causal maps as decision aids in venture capital decision making: Methods and applications," Working Paper No. 291, April 2002, School of Business, University of Kansas. Bayesian causal maps as decision aids in venture capital decision making: Methods and applications (Working Paper No. 291, 2002 — PDF) (113 KB)
  6. Lander, D. M., and P. P. Shenoy, "Modeling and valuing real options using influence diagrams," Working Paper No. 283, June 1999, School of Business, University of Kansas. Modeling and valuing real options using influence diagrams (PDF) (232 KB)
  7. Lepar, V., and P. P. Shenoy, "A comparison of architectures for exact computation of marginals," Working Paper No. 274, February 1997, School of Business, University of Kansas. A comparison of architectures for exact computation of marginals (PDF) (113 KB)
  8. Shafer, G., and P. P. Shenoy, "Local computation in hypertrees," Working Paper No. 201, August 1988, School of Business, University of Kansas. Local computation in hypertrees (PDF) (1.921 MB)