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Sets of probability distributions, independence, and convexity (2012)

  • Authors:
  • USP affiliated authors: COZMAN, FABIO GAGLIARDI - EP
  • USP Schools: EP
  • DOI: 10.1007/s11229-011-9999-0
  • Subjects: PROBABILIDADE; INFERÊNCIA BAYESIANA
  • Language: Inglês
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  • Source:
    • Título do periódico: Synthese
    • ISSN: 0039-7857
    • Volume/Número/Paginação/Ano: v. 186, n. 2, p. 577-600, 2012
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    ISSN: 1516-3598

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    Informações sobre o Citescore
  • Título: Synthese

    ISSN: 0039-7857

    Citescore - 2017: 0.88

    SJR - 2017: 0.966

    SNIP - 2017: 1.089


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    • ABNT

      COZMAN, Fabio Gagliardi. Sets of probability distributions, independence, and convexity. Synthese, Dordrecht, v. 186, n. 2, p. 577-600, 2012. DOI: 10.1007/s11229-011-9999-0.
    • APA

      Cozman, F. G. (2012). Sets of probability distributions, independence, and convexity. Synthese, 186( 2), 577-600. doi:10.1007/s11229-011-9999-0
    • NLM

      Cozman FG. Sets of probability distributions, independence, and convexity. Synthese. 2012 ; 186( 2): 577-600.
    • Vancouver

      Cozman FG. Sets of probability distributions, independence, and convexity. Synthese. 2012 ; 186( 2): 577-600.

    Referências citadas na obra
    Armstrong T. E. (1989) Countably additive full conditional probabilities. Proceedings of the American Mathematical Society 107(4): 977–987
    Berger J. O. (1985) Statistical decision theory and Bayesian analysis. Springer, New York
    Berger J., Moreno E. (1994) Bayesian robustness in bidimensional models: Prior independence. Journal of Statistical Planning and Inference 40: 161–176
    Bernardo J. M., Smith A. F. M. (1994) Bayesian theory. Wiley, New York
    Coletti, G., & Scozzafava, R. (2002). Probabilistic logic in a coherent setting. Trends in logic, 15. Dordrecht: Kluwer.
    Couso, I., Moral, S., & Walley, P. (1999). Examples of independence for imprecise probabilities. In G. de Cooman, F. Cozman, S. Moral, & P. Walley (Eds.), Proceedings of the first international symposium on imprecise probabilities and their applications, Ghent, Belgium (pp. 121–130).
    Couso I., Moral S., Walley P. (2000) A survey of concepts of independence for imprecise probabilities. Risk, Decision and Policy 5: 165–181
    Cowell R. G., Dawid A. P., Lauritzen S. L., Spiegelhalter D. J. (1999) Probabilistic networks and expert systems. Springer, New York
    Cozman F. G. (2000a) Credal networks. Artificial Intelligence 120: 199–233
    Cozman, F. G. (2000b). Separation properties of sets of probabilities. In C. Boutilier & M. Goldszmidt (Eds.), Conference on uncertainty in artificial intelligence, San Francisco (pp. 107–115).
    Cozman, F. G. (2001). Constructing sets of probability measures through Kuznetsov’s independence condition. In Proceedings of the second international symposium on imprecise probabilities and their applications, Ithaca, NY, USA (pp. 104–111).
    Cozman, F. G. (2009). Concentration inequalities and laws of large numbers under epistemic irrelevance. In Proceedings of the sixth international symposium on imprecise probabilities and their applications, Durham, UK.
    Cozman, F. G., & Seidenfeld, T. (2007). Independence for full conditional measures, graphoids and Bayesian networks. Technical report, PMR, Escola Politecnica, Universidade de Sao Paulo, São Paulo, Brazil.
    Cozman F. G., Walley P. (2005) Graphoid properties of epistemic irrelevance and independence. Annals of Mathematics and Artificial Intelligence 45: 173–195
    Dawid A. P. (1979) Conditional independence in statistical theory (with discussion). Journal of the Royal Statistical Society B 41: 1–31
    Dawid A. P. (2001) Separoids: A mathematical framework for conditional independence and irrelevance. Annals of Mathematics and Artificial Intelligence 32(1–4): 335–372
    de Campos, C. P., & Cozman, F. G. (2004). Inference in credal networks using multilinear programming. In E. Onaindia & S. Staab (Eds.), Proceedings of the second starting AI researchers’ symposium (STAIRS), Amsterdam, The Netherlands (pp. 50–61).
    de Campos, C. P., & Cozman, F. G. (2005). The inferential complexity of Bayesian and credal networks. In International joint conference on artificial intelligence (IJCAI), Edinburgh, UK (pp. 1313–1318).
    de Campos C. P., Cozman F. G. (2007) Computing lower and upper expectations under epistemic independence. International Journal of Approximate Reasoning 44(3): 244–260
    de Campos, L., & Moral, S. (1995). Independence concepts for convex sets of probabilities. In P. Besnard & S. Hanks (Eds.), XI conference on uncertainty in artificial intelligence, San Francisco, CA, USA (pp. 108–115).
    de Cooman G., Miranda E. (2008) Weak and strong laws of large numbers for coherent lower previsions. Journal of Statistical Planning and Inference 138(8): 2409–2432
    de Finetti, B. (1974). Theory of probability (Vols. 1–2). New York: Wiley.
    Diaconis P., Freedman D. (1980) de Finetti’s theorem for Markov chains. The Annals of Probability 8(1): 115–130
    Diaconis P., Zabell S. L. (1982) Updating subjective probability. Journal of the American Statistical Association 77(380): 822–829
    Dubins L. E. (1975) Finitely additive conditional probability, conglomerability and disintegrations. Annals of Statistics 3(1): 89–99
    Fagiuoli E., Zaffalon M. (1998) 2U: An exact interval propagation algorithm for polytrees with binary variables. Artificial Intelligence 106(1): 77–107
    Fishburn P. C. (1970) Utility theory for decision making. Wiley, New York
    Gardenfors P., Sahlin N. E. (1982) Unreliable probabilities, risk taking and decision making. Synthese 53: 361–386
    Geiger D., Verma T., Pearl J. (1990) d-Separation: From theorems to algorithms. Uncertainty in Artificial Intelligence 5: 139–148
    Giron F. J., Rios S. (1980) Quasi-Bayesian behaviour: A more realistic approach to decision making?. In: Bernardo J. M., DeGroot J. H., Lindley D. V., Smith A. F. M. (eds) Bayesian statistics. University Press, Valencia, pp 17–38
    Hajek A. (2003) What conditional probability could not be. Synthese 137: 273–323
    Halpern, J. Y. (2001). Lexicographic probability, conditional probability, and nonstandard probability. In Proceedings of the eighth conference on theoretical aspects of rationality and knowledge (pp. 17–30).
    Hammond P. J. (1994) Elementary non-Archimedean representations of probability for decision theory and games. In: Humphreys P. (ed.) Patrick suppes: Scientific philosopher (Vol. 1). Kluwer, Dordrecht, pp 25–59
    Heath D., Sudderth W. (1976) de Finetti’s theorem on exchangeable variables. The American Statistician 30(4): 188–189
    Jeffrey R. (1965) The logic of decision. McGraw-Hill, New York
    Kadane, J. B., Schervish, M. J., & Seidenfeld, T. (2004). A Rubinesque theory of decision. In A festschrift for Herman Rubin (pp. 45–55). Beachwood, OH: Institute of Mathematical Statistics.
    Keynes J. M. (1921) A treatise on probability. Macmillan and Co, London
    Kikuti, D., Cozman, F. G., & de Campos, C. P. (2005). Partially ordered preferences in decision trees: Computing strategies with imprecision in probabilities. In IJCAI workshop on advances in preference handling, Edinburgh, UK (pp. 118–123).
    Krauss P. (1968) Representation of conditional probability measures on Boolean algebras. Acta Mathematica Academiae Scientiarum Hungaricae 19(3–4): 229–241
    Kuznetsov, V. P. (1991). Interval statistical methods. Radio i Svyaz Publ (in Russian).
    Kuznetsov, V. P. (1995). Auxiliary problems of statistical data processing: Interval approach. In Extended abstracts of APIC95: International workshop on applications of interval computations (pp. 123–129).
    Kyburg, Jr., H., & Pittarelli, M. (1992a). Some problems for convex Bayesians. In Conference on uncertainty in artificial intelligence (pp. 149–154).
    Kyburg, Jr., H. E., & Pittarelli, M. (1992b). Set-based Bayesianism. technical report UR CSD; TR 407. University of Rochester, Computer Science Department. http://hdl.handle.net/1802/765 .
    Kyburg H. E., Pittarelli M. (1996) Set-based Bayesianism. IEEE Transactions on Systems, Man and Cybernetics A 26(3): 324–339
    Lad F. (1996) Operational subjective statistical methods: A mathematical, philosophical, and historical, and introduction. Wiley, New York
    Levi I. (1980) The enterprise of knowledge. MIT Press, Cambridge, MA
    Moral S. (2005) Epistemic irrelevance on sets of desirable gambles. Annals of Mathematics and Artificial Intelligence 45(1–2): 197–214
    Moral S., Cano A. (2002) Strong conditional independence for credal sets. Annals of Mathematics and Artificial Intelligence 35: 295–321
    Pearl J. (1988) Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Mateo, CA
    Schervish M. J. (1995) Theory of statistics. Springer, New York
    Schervish, M., Seidenfeld, T., Kadane, J., & Levi, I. (2003). Extensions of expected utility theory and some limitations of pairwise comparisons. In J.-M. Bernard, T. Seidenfeld, & M. Zaffalon (Eds.), Proceedings of the third international symposium on imprecise probabilities and their applications (pp. 496–510).
    Seidenfeld, T. (2001). Remarks on the theory of conditional probability: Some issues of finite versus countable additivity. In Statistics–philosophy, recent history, and relations to science (pp. 167–177). Dordrecht: Kluwer.
    Seidenfeld, T. (2007). Conditional independence, imprecise probabilities, null-events and graph-theoretic models. Seimour Geisser Distinguished Lecture, School of Statistics, University of Minnesota, Minneapolis, MN.
    Seidenfeld T., Schervish M. J., Kadane J. B. (1990) Decisions without ordering. In: Sieg W. (eds) Acting and reflecting. Kluwer, Dordrecht, pp 143–170
    Seidenfeld T., Schervish M. J., Kadane J. B. (2001) Improper regular conditional distributions. The Annals of Probability 29(4): 1612–1624
    Seidenfeld, T., Schervish, M., & Kadane, J. (2007). Coherent choice functions under uncertainty. In International symposium on imprecise probability: Theories and applications, Prague, Czech Republic.
    Sen A. (1977) Social choice theory: A reexamination. Econometrica 45: 53–89
    Shafer G. (1976) A mathematical theory of evidence. Princeton University Press, Princeton, NJ
    Smith C. A. B. (1961) Consistency in statistical inference and decision. Journal Royal Statistical Society B 23: 1–25
    Troffaes M. (2004) Decision making with imprecise probabilities: A short review. SIPTA Newsletter 2: 4–7
    Utkin, L. V., & Augustin, T. (2005). Powerful algorithms for decision making under prior information and general ambiguity attitudes. In Proceedings of the fourth international symposium on imprecise probabilities and their applications (pp. 349–358).
    Vantaggi, B. (2001). Graphical models for conditional independence structures. In Second international symposium on imprecise probabilities and their applications (pp. 332–341).
    Walley, P. (1982). The elicitation and aggregation of beliefs. Technical report 23, Department of Statistics, University of Warwick, Warwick, UK
    Walley P. (1991) Statistical reasoning with imprecise probabilities. Chapman and Hall, London
    Walley P., Fine T. L. (1982) Towards a frequentist theory of upper and lower probability. Annals of Statistics 10(3): 741–761
    Weichselberger K. (2000) The theory of interval-probability as a unifying concept for uncertainty. International Journal of Approximate Reasoning 24(2–3): 149–170
    Weichselberger, K., Augustin, T., & Wallner, A. (2001). Elementare Grundbegriffe einer allgemeineren Wahrscheinlichkeitsrechnung I: Intervallwahrscheinlichkeit als umfassendes Konzept. Heidelberg: Physica-Verlag.
    Williams, P. M. (1975). Coherence, strict coherence and zero probabilities. In Fifth international congress in logic, methodology and philosophy of science (Vol. VI, pp. 29–30).
    Williams P. M. (2007) Notes on conditional previsions. International Journal of Approximate Reasoning 44: 366–383
    Yaghlane B. B., Smets P., Mellouli K. (2002a) Belief function independence: I. The marginal case. International Journal of Approximate Reasoning 29(1): 47–70
    Yaghlane B. B., Smets P., Mellouli K. (2002b) Belief function independence: II. The conditional case. International Journal of Approximate Reasoning 31(1–2): 31–75
    Zaman A. (1986) A finite form of de Finetti’s theorem for stationary Markov exchangeability. The Annals of Probability 14(4): 1418–1427

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