| 书目名称 | Fuzzy Probability and Statistics | | 编辑 | James J. Buckley | | 视频video | | | 概述 | Clear and didactic book about fuzzy probabilities and statistics useful for students and researchers | | 丛书名称 | Studies in Fuzziness and Soft Computing | | 图书封面 |  | | 描述 | This book combines material from our previous books FP (Fuzzy Probabilities: New Approach and Applications,Physica-Verlag, 2003) and FS (Fuzzy Statistics, Springer, 2004), plus has about one third new results. From FP we have material on basic fuzzy probability, discrete (fuzzy Poisson,binomial) and continuous (uniform, normal, exponential) fuzzy random variables. From FS we included chapters on fuzzy estimation and fuzzy hypothesis testing related to means, variances, proportions, correlation and regression. New material includes fuzzy estimators for arrival and service rates, and the uniform distribution, with applications in fuzzy queuing theory. Also, new to this book, is three chapters on fuzzy maximum entropy (imprecise side conditions) estimators producing fuzzy distributions and crisp discrete/continuous distributions. Other new results are: (1) two chapters on fuzzy ANOVA (one-way and two-way); (2) random fuzzy numbers with applications to fuzzy Monte Carlo studies; and (3) a fuzzy nonparametric estimator for the median. | | 出版日期 | Book 2006 | | 关键词 | ANOVA; Estimator; Fuzzy; Fuzzy Probabilities; Fuzzy Statistics; Maple; Median; Probability theory; Random va | | 版次 | 1 | | doi | https://doi.org/10.1007/3-540-33190-5 | | isbn_softcover | 978-3-642-06809-6 | | isbn_ebook | 978-3-540-33190-2Series ISSN 1434-9922 Series E-ISSN 1860-0808 | | issn_series | 1434-9922 | | copyright | Springer-Verlag Berlin Heidelberg 2006 |
| 1 |
Front Matter |
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Abstract
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| 2 |
,Introduction, |
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| 3 |
,Fuzzy Sets, |
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,Fuzzy Probability Theory, |
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,Discrete Fuzzy Random Variables, |
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,Continuous Fuzzy Random Variables, |
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,Estimate ,, Variance Known, |
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,Estimate ,, Variance Unknown, |
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,Estimate ,, Binomial Population, |
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,Estimate , from a Normal Population, |
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| 11 |
,Fuzzy Arrival/Service Rates, |
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| 12 |
,Fuzzy Uniform, |
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| 13 |
,Fuzzy Max Entropy Principle, |
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Abstract
We solved the maximum entropy principle with imprecise side-conditions, which were modeled as fuzzy sets, producing fuzzy probability distributions. It seems very natural if you start with a fuzzy mean, variance, etc, you need to end up with a fuzzy probability distribution. Fuzzy probability distributions produce fuzzy means, variances, etc. In the next two chapters we restrict the solutions to be crisp (not fuzzy).
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,Max Entropy: Crisp Discrete Solutions, |
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Abstract
In this chapter we showed how to solve the maximum entropy problem with imprecise side-conditions for a crisp (non-fuzzy) discrete probability distribution. The next step would be to solve for a crisp continuous probability density. That is the topic of the next chapter.
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,Max Entropy: Crisp Continuous Solutions, |
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,Tests on ,, Variance Known, |
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,Tests on ,, Variance Unknown, |
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,Tests on , for a Binomial Population, |
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,Tests on ,, Normal Population, |
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,Fuzzy Correlation, |
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发表于 2025-3-21 20:05:00
