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 19.
... chapters 5 monograph. he algorithms for manipulating random variables (e.g., adding, multi, transforming, ordering) symbolically result in an entire class of new ems that can now be addressed. The implementation of these algorithms ple ...
... chapter, then in Chapter 2 rethe Maple data structures and functions necessary to implement APPL. s followed by a discussion of the development of the algorithms (Chapters ir continuous random variables and Chapters 6–8 for discrete ...
... Chapters 3 and 6). The first sublist gives the funcDnal form of the PDF, the second sublist gives the support, and e third sublist indicates that the random variable being defined is intinuous and that the function in the first sublist ...
... Chapters 3–5, considers continuous m variables. The data structure used for defining a continuous ranWariable is defined in Chapter 3. Chapters 4 and 5 contain examples of thms devised for manipulating continuous random variables. Chapter ...
... Chapters 6–8, considers discrete ranWariables. The data structure that we have used for defining a discrete m variable is defined in Chapter 6. Chapters 7 and 8 contain examples Drithms for manipulating discrete random variables. Chapter ...
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 |