Fuzzy probabilities: new approach and applications
(eBook)

Book Cover
Published:
Berlin ; New York : Springer, ©2005., Berlin ; New York : Springer, [2005].
Format:
eBook
ISBN:
3540250336, 9783540250333, 9783540323884, 3540323880, 1280625562, 9781280625565, 9786610625567, 6610625565
Content Description:
1 online resource (xi, 164 pages) : illustrations.
Status:
Available Online
Description

In probability and statistics we often have to estimate probabilities and parameters in probability distributions using a random sample. Instead of using a point estimate calculated from the data we propose using fuzzy numbers which are constructed from a set of confidence intervals. In probability calculations we apply constrained fuzzy arithmetic because probabilities must add to one. Fuzzy random variables have fuzzy distributions. A fuzzy normal random variable has the normal distribution with fuzzy number mean and variance. Applications are to queuing theory, Markov chains, inventory control, decision theory and reliability theory.

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APA Citation (style guide)

Buckley, J. J. (2005). Fuzzy probabilities: new approach and applications. Berlin ; New York, Springer.

Chicago / Turabian - Author Date Citation (style guide)

Buckley, James J., 1936-. 2005. Fuzzy Probabilities: New Approach and Applications. Berlin ; New York, Springer.

Chicago / Turabian - Humanities Citation (style guide)

Buckley, James J., 1936-, Fuzzy Probabilities: New Approach and Applications. Berlin ; New York, Springer, 2005.

MLA Citation (style guide)

Buckley, James J. Fuzzy Probabilities: New Approach and Applications. Berlin ; New York, Springer, 2005.

Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
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Language:
English
UPC:
10.1007/3-540-32388-0.

Notes

Bibliography
Includes bibliographical references and index.
Restrictions on Access
Access is restricted to subscribing institutions.
Description
In probability and statistics we often have to estimate probabilities and parameters in probability distributions using a random sample. Instead of using a point estimate calculated from the data we propose using fuzzy numbers which are constructed from a set of confidence intervals. In probability calculations we apply constrained fuzzy arithmetic because probabilities must add to one. Fuzzy random variables have fuzzy distributions. A fuzzy normal random variable has the normal distribution with fuzzy number mean and variance. Applications are to queuing theory, Markov chains, inventory control, decision theory and reliability theory.
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Last Sierra Extract TimeMar 20, 2024 05:20:14 PM
Last File Modification TimeMar 20, 2024 05:26:59 PM
Last Grouped Work Modification TimeMar 20, 2024 05:20:21 PM

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