Information-theoretic methods for estimating complicated probability distributions [electronic resource] / Zhi Zong.
- 作者： Zong, Zhi.
- 出版： Amsterdam ;Boston : Elsevier 2006.
- 叢書名： Mathematics in science and engineering ;v. 207
- 主題： Distribution (Probability theory) , Information theory. , Approximation theory.
- 版本：1st ed.
- ISBN： 9780444527967 (electronic bk.) 、 9780444527967
- URL：
電子書
- 書目註：Includes bibliographical references (p. 289-293) and index.
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讀者標籤：
- 系統號： 005212574 | 機讀編目格式
館藏資訊
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.-