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 18.
... symbolic calculations. The purpose s monograph is to address the plight of the probabilist by providing alms to ... symbolically result in an entire class of new ems that can now be addressed. The implementation of these algorithms ple ...
... Symbolic Computations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 .5 Data Types . . . . . . . . .
... symbolic parameters. Unlike most of the earlier ples, this example can be worked by hand, then the solution can be ed using APPL. Furthermore, a student can change any of the arguments problem (e.g., the sample size or critical value) ...
... symbolic ssions, plotting, and programming, just to name a few of the basics. is, simply, a set of supplementary Maple commands and procedures ugments the existing computer algebra system. In effect, APPL takes apabilities of Maple and ...
... Symbolic Computations if Maple's main strengths is its ability to manipulate symbolic expres– Symbols can be treated in the same way that numbers were in the us section and can do much more. Entering a constant or variable folby a ...
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 |