Computational Probability Applications

Computational Probability Applications edited by Andrew G. Glen, Lawrence M. Leemis.

This focuses on the developing field of building probability models with the power of symbolic algebra systems. The book combines the uses of symbolic algebra with probabilistic/stochastic application and highlights the applications in a variety of contexts. The research explored in each chapter is...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Glen, Andrew G. (Editor), Leemis, Lawrence M. (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Series:International Series in Operations Research & Management Science, 247
Springer eBook Collection.
Subjects:
Operations research.
Decision making.
Statistics .
Probabilities.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Accurate Estimation with One Order Statistic
  • On the Inverse Gamma as a Survival Distribution
  • Order Statistics in Goodness-of-Fit Testing
  • The "Straightforward" Nature of Arrival Rate Estimation?
  • Survival Distributions Based on the Incomplete Gamma Function Ratio
  • An Inference Methodology for Life Tests with Full Samples or Type II Right Censoring
  • Maximum Likelihood Estimation Using Probability Density Functions of Order Statistics
  • Notes on Rank Statistics
  • Control Chart Constants for Non-Normal Sampling
  • Linear Approximations of Probability Density Functions
  • Univariate Probability Distributions
  • Moment-Ratio Diagrams for Univariate Distributions
  • The Distribution of the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling Test Statistics for Exponential Populations with Estimated Parameters
  • Parametric Model Discrimiation for Heavily Censored Survival Data
  • Lower Confidence Bounds for System Reliability from Binary Failure Data Using Bootstrapping. .

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