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 34.
... contain exercises and examples geared toward students, than researchers. Second, the emphasis in most of these texts is much er than the emphasis being proposed here. For example, Parlar and ng consider all of OR/MS, rather than the ...
... contains a brief review of Maple syntax, data ures, and programming constructs used to write the procedures that rise APPL. We survey only a small portion of the Maple language. he second part of the monograph, Chapters 3–5, considers ...
... contain examples Drithms for manipulating discrete random variables. Chapter 7 considers of discrete random variables ... contains applications in discrete-event simulation, including random number g, input modeling, and goodness-of-fit ...
... contains guidelines for using Maple, discusses the Maple commands that are used in APPL programming. reading this chapter, an APPL user gains the knowledge necessary to y the APPL code to meet his or her particular needs. We will start ...
... contain alphanumeric characters and underscores, it it cannot start with a number. nce a variable is assigned to an expression, it remains that expression ntil changed or cleared. variable name can be cleared by assigning variable ...
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 |
Citi izdevumi - Skatīt visu
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 |