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- W4243960124 abstract "In previous chapters, we have seen how various nonmonotonic formalisms can be used to model uncertain reasoning. However, none of these formalisms gives us any way of representing degrees of uncertainty associated with beliefs or modes of inference. And it is not difficult to think of applications of artificial intelligence systems which require the ability to quantify uncertainty. The AI literature contains a variety of formalisms for representing degrees of uncertainty. Of these, one has by far the longest history (dating back to the 17th century) and the best theoretical motivation: probability theory. In this chapter, we shall discuss probability theory and the problems arising in developing computer systems which reason probabilistically." @default.
- W4243960124 created "2022-05-12" @default.
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- W4243960124 date "1994-01-01" @default.
- W4243960124 modified "2023-09-25" @default.
- W4243960124 title "Probabilistic Inference" @default.
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- W4243960124 doi "https://doi.org/10.1007/978-1-349-13277-5_8" @default.
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