A Comparison of Some Rules for Probabilistic Reasoning

Abstract Generalized Bayesian conditionals and Dempster-Shafer's conditionals are considered as probabilistic kinematics which hold under different conditions. In particular, generalized Bayes can be applied whenever the available evidence allows to partition the frame of reference. It will be pointed out how, in this case, it is always possible to get a probability function by a belief function by means of minimum (relative) entropy kinematics.

[1]  Bas C. van Fraassen,et al.  Rational Belief and Probability Kinematics , 1980 .

[2]  Peter C. Cheeseman,et al.  A Method of Computing Generalized Bayesian Probability Values for Expert Systems , 1983, IJCAI.

[3]  Judea Pearl,et al.  Probabilistic reasoning using graphs , 1987, International Conference on Information Processing and Management of Uncertainty.

[4]  D. Dubois,et al.  Recent models of uncertainty and imprecision as a basis for decision theory:towards less normative frameworks , 1986 .

[5]  P. M. Williams.  Bayesian Conditionalisation and the Principle of Minimum Information , 1980, The British Journal for the Philosophy of Science.

[6]  Judea Pearl,et al.  A Computational Model for Causal and Diagnostic Reasoning in Inference Systems , 1983, IJCAI.

[7]  G. Shafer.  Jeffrey's Rule of Conditioning , 1981, Philosophy of Science.

[8]  John F. Lemmer,et al.  Efficient Minimum Information Updating for Bayesian Inferencing in Expert Systems , 1982, AAAI.

[9]  Hartry Field.  A Note on Jeffrey Conditionalization , 1978, Philosophy of Science.

[10]  John D. Lowrance,et al.  An Inference Technique for Integrating Knowledge from Disparate Sources , 1981, IJCAI.

[11]  Zoltan Domotor.  Probability Kinematics and Representation of Belief Change , 1980, Philosophy of Science.

[12]  Jeffrey A. Barnett,et al.  Computational Methods for a Mathematical Theory of Evidence , 1981, IJCAI.

[13]  P. Diaconis,et al.  Updating Subjective Probability , 1982 .

[14]  Richard O. Duda,et al.  Subjective bayesian methods for rule-based inference systems , 1976, AFIPS '76.