Inference and Learning in Probabilistic Logic Programs with Continuous Random Variables
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of the Dissertation Inference and Learning in Probabilistic Logic Programs with Continuous Random Variables
منابع مشابه
Parameter Learning in PRISM Programs with Continuous Random Variables
Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya’s PRISM, Poole’s ICL, De Raedt et al’s ProbLog and Vennekens et al’s LPAD, combines statistical and logical knowledge representation and inference. Inference in these languages is based on enumerative construction of proofs over logic programs. Consequently, these languages permit very limited use of random variables with con...
متن کاملInference in probabilistic logic programs with continuous random variables
Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya’s PRISM, Poole’s ICL, Raedt et al’s ProbLog and Vennekens et al’s LPAD, is aimed at combining statistical and logical knowledge representation and inference. However, the inference techniques used in these works rely on enumerating sets of explanations for a query answer. Consequently, these languages permit very limited use ...
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cplint on SWISH is a web application for probabilistic logic programming. It allows users to perform inference and learning using just a web browser, with the computation performed on the server. In this paper we report on recent advances in the system, namely the inclusion of algorithms for computing conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreo...
متن کاملcplint on SWISH: Probabilistic Logical Inference with a Web Browser
cplint on SWISH is a web application that allows users to perform reasoning tasks on probabilistic logic programs. Both inference and learning systems can be performed: conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on ...
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In the last decade remarkable progress has been made on combining statistical machine learning techniques, reasoning under uncertainty, and relational representations. The branch of Artificial Intelligence working on the synthesis of these three areas is known as statistical relational learning or probabilistic logic learning. ProbLog, one of the probabilistic frameworks developed, is an extens...
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تاریخ انتشار 2012