Computational Probability: Algorithms and Applications in the Mathematical SciencesSpringer Science & Business Media, 2008. gada 8. janv. - 222 lappuses Computational probability encompasses data structures and algorithms that have emerged over the past decade that allow researchers and students to focus on a new class of stochastic problems. COMPUTATIONAL PROBABILITY is the first book that examines and presents these computational methods in a systematic manner. The techniques described here address problems that require exact probability calculations, many of which have been considered intractable in the past. The first chapter introduces computational probability analysis, followed by a chapter on the Maple computer algebra system. The third chapter begins the description of APPL, the probability modeling language created by the authors. The book ends with three applications-based chapters that emphasize applications in survival analysis and stochastic simulation. The algorithmic material associated with continuous random variables is presented separately from the material for discrete random variables. Four sample algorithms, which are implemented in APPL, are presented in detail: transformations of continuous random variables, products of independent continuous random variables, sums of independent discrete random variables, and order statistics drawn from discrete populations. The APPL computational modeling language gives the field of probability a strong software resource to use for non-trivial problems and is available at no cost from the authors. APPL is currently being used in applications as wide-ranging as electric power revenue forecasting, analyzing cortical spike trains, and studying the supersonic expansion of hydrogen molecules. Requests for the software have come from fields as diverse as market research, pathology, neurophysiology, statistics, engineering, psychology, physics, medicine, and chemistry. |
No grāmatas satura
1.–5. rezultāts no 43.
... defines X as a U(0,1) random variable. he second line defines the random variable Y as the sum of 10 iid ndom variables, each having the same distribution as X. Finally, e last line evaluates the cumulative distribution function (CDF) ...
... defined in the first APPL statement. he critical value c is defined next. The Maple assume procedure dehes the parameter space 6 = 0. The fact that the population distriution is non-standard requires the random variable X to be defined ...
... defined in a piecewise fashion, he triangular distribution? In this case an Outside loop must be added Transform procedure in Order to do the appropriate bookkeeping so all of the density gets transformed from the distribution of X to ...
... defined in Chapter 6. Chapters 7 and 8 contain examples Drithms for manipulating discrete random variables. Chapter 7 considers of discrete random variables and Chapter 8 considers the distribution of statistics drawn from discrete ...
... define a function, then the On must be formally defined, and two techniques for doing so appear in n 2.4. metimes assumptions must be made on variables in Order to set variable rties or relationships. A common use of the assume function ...
Saturs
6 | |
Solving Equations | 20 |
Simple Algorithms | 37 |
Examples | 50 |
roducts of Random Variables | 55 |
ata Structures and Simple Algorithms | 71 |
ums of Independent Random Variables | 92 |
Algorithm | 106 |
rder Statistics | 119 |
teliability and Survival Analysis 135 | 133 |
to chastic Simulation 153 | 152 |
ther Applications | 185 |
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Computational Probability: Algorithms and Applications in the Mathematical ... John H. Drew,Diane L. Evans,Andrew G. Glen,Lawrence Leemis Priekšskatījums nav pieejams - 2007 |
Computational Probability: Algorithms and Applications in the Mathematical ... John H. Drew,Diane L. Evans,Andrew G. Glen,Lawrence Leemis Priekšskatījums nav pieejams - 2010 |