Author:
Edition: 1
Publisher: Chapman and Hall/CRC
Binding: Hardcover
ISBN: 1584885939
Statistical Methods for Spatio-Temporal Systems (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. Medical books Statistical Methods for Spatio-Temporal Systems . BR>
Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data Medical books Statistical Methods Of Spatio-temporal Systems: Vol 107 Finkenstadt, Barbel (edi. author barbel finkenstadt format hardback language english publication year 20 10 2006 series chapman hall crc monographs on statistics applied probability subject mathematics sciences subject 2 mathematics title statistical methods of spatio temporal systems vol 107 author finkenstadt barbel editor held leonhard editor isham valerie editor publisher chapman hall publication date oct 31 2006 pages 286 binding hardcover edition 1 st dimensions 6 25 wx 9 25 hx 1 00 d isbn 1584885939 subject math
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author barbel finkenstadt format hardback language english publication year 20 10 2006 series chapman hall crc monographs on statistics applied probability subject mathematics sciences subject 2 mathematics title statistical methods of spatio temporal systems vol 107 author finkenstadt barbel editor held leonhard editor isham valerie editor publisher chapman hall publication date oct 31 2006 pages 286 binding hardcover edition 1 st dimensions 6 25 wx 9 25 hx 1 00 d isbn 1584885939 subject math
The first statistics book devoted to spatio-temporal models, Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling that will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. The book describes recent advances and presents a variety of statistical methods, including likelihood-based, nonparametric smoothing, spectral, Fourier, wavelet, and Markov chain Monte Carlo. The methods are illustrated with color images as well as real-world examples, case stud
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Store Search search Title, ISBN and Author Statistical Methods for Spatio-Temporal Systems by Barbel Finkenstadt Estimated delivery 3-12 business days Format Hardcover Condition Brand New Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. Contributed by leading researchers in the field, each
Medical Book Statistical Methods for Spatio-Temporal Systems
BR>
Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time covariance functions. The contributors then describe stochastic and statistical models that are used to generate simulated rainfall sequences for hydrological use, such as flood risk assessment. The final chapter explores Gaussian Markov random field specifications and Bayesian computational inference via Gibbs sampling and Markov chain Monte Carlo, illustrating the methods with a variety of data examples, such as temperature surfaces, dioxin concentrations, ozone concentrations, and a well-established deterministic dynamical weather model.
