Probabilistic Graphical Models - Studienarbeit - Florian
نویسندگان
چکیده
solemnly declare that I have written this master thesis independently, and that I have not made use of any aid other than those acknowledged in this master thesis. Neither this master thesis, nor any other similar work, has been previously submitted to any examination board.
منابع مشابه
Rule-based joint fuzzy and probabilistic networks
One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...
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There is an increasing demand for managing and reasoning about probabilistic data including for example noisy sensor data arising from ubiquitous computing. On top this data often includes complex correlation patterns. Currently Probabilistic Database Systems support uncertainty usually at individual tuple or attribute level allowing for fine-grained uncertainty but often also resulting in unne...
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A method for calculating some profile likelihood inferences in probabilistic graphical models is presented and applied to the problem of classification. It can also be interpreted as a method for obtaining inferences from hierarchical networks, a kind of imprecise probabilistic graphical models.
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We look at probabilistic logic programs as a specification language for probabilistic models, and study their interpretation and complexity. Acyclic programs specify Bayesian networks, and, depending on constraints on logical atoms, their inferential complexity reaches complexity classes #P, #NP, and even #EXP. We also investigate (cyclic) stratified probabilistic logic programs, showing that t...
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