Information-theoretic Methods for Estimating Complicated Probability Distributions - Info and Reading Options
By Zhi Zong
"Information-theoretic Methods for Estimating Complicated Probability Distributions" was published by Elsevier Science in October 16, 2006, the book is classified in Mathematics genre, it has 299 pages and the language of the book is English.
“Information-theoretic Methods for Estimating Complicated Probability Distributions” Metadata:
- Title: ➤ Information-theoretic Methods for Estimating Complicated Probability Distributions
- Author: Zhi Zong
- Language: English
- Number of Pages: 299
- Is Family Friendly: Yes - No Mature Content
- Publisher: Elsevier Science
- Publish Date: October 16, 2006
- Genres: Mathematics
“Information-theoretic Methods for Estimating Complicated Probability Distributions” Subjects and Themes:
- Subjects: ➤ Distribution (Probability theory) - Information theory - Approximation theory - Probabilities - Packed towers - Mass transfer - Colonnes à garnissage - Transfert de masse - TECHNOLOGY & ENGINEERING - Chemical & Biochemical - SCIENCE - Chemistry - Industrial & Technical - Functional equations - Engineering mathematics - Nonlinear theories - Difference equations - Differentiable dynamical systems
Edition Identifiers:
- Google Books ID: EY7_kQAACAAJ
- The Open Library ID: OL7531184M - OL8130969W
- Online Computer Library Center (OCLC) ID: 647617000 - 70408077 - 156755844
- Library of Congress Control Number (LCCN): 2006049568
- ISBN-13: 9780444527967
- ISBN-10: 0444527966
- All ISBNs: 0444527966 - 9780444527967
AI-generated Review of “Information-theoretic Methods for Estimating Complicated Probability Distributions”:
Snippets and Summary:
In engineering, physical and social science applications, however, the distributions of many random variables or random vectors are so complicated that they do not fit the simple distribution forms at al.
"Information-theoretic Methods for Estimating Complicated Probability Distributions" Description:
Google Books:
In engineering, physical and social science applications, however, the distributions of many random variables or random vectors are so complicated that they do not fit the simple distribution forms at al. Exact estimation of the probability distribution of a random variable is very important. Take stock market prediction for example. Gaussian distribution is often used to model the fluctuations of stock prices. If such fluctuations are not normally distributed, and we use the normal distribution to represent them, how could we expect our prediction of stock market is correct? Another case well exemplifying the necessity of exact estimation of probability distributions is reliability engineering. Failure of exact estimation of the probability distributions under consideration may lead to disastrous designs.-
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