Stochastic Modeling of Scientific Data (Record no. 3009)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 01763 a2200301 4500 |
| 001 - CONTROL NUMBER | |
| control field | 0367449005 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250317100417.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250312042020xx eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780367449001 |
| 037 ## - SOURCE OF ACQUISITION | |
| Source of stock number/acquisition | Taylor & Francis |
| Terms of availability | GBP 61.99 |
| Form of issue | BB |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | 01 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PBT |
| Source | thema |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PS |
| Source | thema |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | T |
| Source | thema |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PBT |
| Source | bic |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PS |
| Source | bic |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | T |
| Source | bic |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | MAT029010 |
| Source | bisac |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | MAT029000 |
| Source | bisac |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | 519.2 |
| Source | bisac |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Peter Guttorp |
| 245 10 - TITLE STATEMENT | |
| Title | Stochastic Modeling of Scientific Data |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Name of publisher, distributor, etc. | Chapman and Hall/CRC |
| Date of publication, distribution, etc. | 20201218 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 384 p |
| 520 ## - SUMMARY, ETC. | |
| Expansion of summary note | Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing feature of this work is that, in addition to probability theory, it contains statistical aspects of model fitting and a variety of data sets that are either analyzed in the text or used as exercises. Markov chain Monte Carlo methods are introduced for evaluating likelihoods in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use of informal language that makes the topic more accessible to non-mathematicians. The combinations of hard science topics with stochastic processes and their statistical inference puts it in a new category of probability textbooks. The numerous examples and exercises are drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics. |
No items available.