Salīdzināt metodes

	Granulārā skaitļošana (informācijas granulēšana)	Neprecīza varbūtība
Nozare	Mīkstā skaitļošana	Mīkstā skaitļošana
Saime	Machine learning	Bayesian methods
Izcelsmes gads	1997	1991
Autors	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao	Peter Walley
Tips	Framework for multi-granularity information processing	Set-valued probability model
Pirmavots	Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.	Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall. ISBN: 978-0-412-28660-5
Citi nosaukumi	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama	Lower-Upper Probability, Robust Bayesian Analysis, Credal Set Theory, Belirsiz Olasılık
Saistītās	3	3
Kopsavilkums	Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.	Imprecise probability is a generalization of standard probability theory that represents epistemic uncertainty through sets of probability measures, called credal sets, rather than a single precise distribution. Introduced systematically by Peter Walley in his 1991 monograph, the framework characterizes beliefs via lower and upper probabilities (or previsions), bracketing the range of plausible probability assignments when available information is insufficient to determine a unique measure.