Monte Carlo Statistical Methods /
Until the advent of powerful and accessible computing methods, the experimenter was often confronted with a difficult choice. Either describe an accurate model of a phenomenon, which would usually preclude the computation of explicit answers, or choose a standard model which would allow this computa...
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| Format: | eBook |
| Language: | English |
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New York, NY :
Springer New York,
1999.
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| Series: | Springer texts in statistics.
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| Online Access: | Connect to the full text of this electronic book |
| Summary: | Until the advent of powerful and accessible computing methods, the experimenter was often confronted with a difficult choice. Either describe an accurate model of a phenomenon, which would usually preclude the computation of explicit answers, or choose a standard model which would allow this computation, but may not be a close representation of a realistic model. This dilemma is present in many branches of statistical applications, for example in electrical engineering, aeronautics, biology, networks, and astronomy. Markov chain Monte Carlo methods have been developed to provide realistic models. |
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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (xxi, 509 pages) |
| ISBN: | 9781475730715 (electronic bk.) 1475730713 (electronic bk.) |
| ISSN: | 1431-875X |