Abstract
This paper addresses the problem of finding a Bayesian net representation of the probability function that agrees with the distributions of multiple consistent datasets and otherwise has maximum entropy. We give a general algorithm which is significantly more efficient than the standard brute-force approach. Furthermore, we show that in a wide range of cases such a Bayesian net can be obtained without solving any optimisation problem.
Item Type: | Journal article |
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Faculties: | Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) > Epistemology |
Subjects: | 100 Philosophy and Psychology > 100 Philosophy 100 Philosophy and Psychology > 120 Epistemology |
Language: | English |
Item ID: | 42607 |
Date Deposited: | 12. Mar 2018, 14:03 |
Last Modified: | 04. Nov 2020, 13:18 |