Probabilistic Belief Contraction

Autor:	Arthur Ramer, Abhaya C. Nayak, Raghav Ramachandran
Rok vydání:	2012
Předmět:	Possibility distribution Philosophy Mathematical optimization Artificial Intelligence Principle of maximum entropy Bayesian probability Theory of computation Probabilistic logic Min entropy Entropy (information theory) Joint entropy Algorithm Mathematics
Zdroj:	Minds and Machines. 22:325-351
ISSN:	1572-8641 0924-6495
DOI:	10.1007/s11023-012-9284-0
Popis:	Probabilistic belief contraction has been a much neglected topic in the field of probabilistic reasoning. This is due to the difficulty in establishing a reasonable reversal of the effect of Bayesian conditionalization on a probabilistic distribution. We show that indifferent contraction, a solution proposed by Ramer to this problem through a judicious use of the principle of maximum entropy, is a probabilistic version of a full meet contraction. We then propose variations of indifferent contraction, using both the Shannon entropy measure as well as the Hartley entropy measure, with an aim to avoid excessive loss of beliefs that full meet contraction entails.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e41141e99f27f730a446638105e36814 https://doi.org/10.1007/s11023-012-9284-0 Zobrazit plný text záznamu Plný text ve formátu PDF