Probabilistic inference with noisy-threshold models based on a CP tensor decomposition
نویسندگان
چکیده
منابع مشابه
Probabilistic inference with noisy-threshold models based on a CP tensor decomposition
The specification of conditional probability tables (CPTs) is a difficult task in the construction of probabilistic graphical models. Several types of canonical models have been proposed to ease that difficulty. Noisy-threshold models generalize the two most popular canonical models: the noisy-or and the noisy-and. When using the standard inference techniques the inference complexity is exponen...
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Conditional probability tables (CPTs) of threshold functions represent a generalization of two popular models – noisy-or and noisy-and. They constitute an alternative to these two models in case they are too rough. When using the standard inference techniques the inference complexity is exponential with respect to the number of parents of a variable. In case the CPTs take a special form (in thi...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2014
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2013.12.002