Isbn: 9780691121789 - models for ecological data: an introduction (24 resultados)

Hardcover. Condición: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Hardback. Condición: Good. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful statistical tools for making sense of ecological processes. This work introduces them to these methods in modeling and computation. It also describes how complexity can motivate a shift to Bayesian methods. …

Condición: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condición: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardback. Condición: Good. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful statistical tools for making sense of ecological processes. This work introduces them to these methods in modeling and computation. It also describes how complexity can motivate a shift to Bayesian methods. …

hardcover. Condición: Good. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1750grams, ISBN:9780691121789. …

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1750grams, ISBN:9780691121789.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New. 2007. Hardcover. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful statistical tools for making sense of ecological processes. This work introduces them to these methods in modeling and computation. It also describes how complexity can motivate a shift to Bayesian methods. Num Pages: 632 pages, 163 line illus. 21 tables. BIC Classification: JHBC; RN. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 255 x 210 x 40. Weight in Grams: 1680. . . . . .…

Condición: As New. Unread book in perfect condition.

Condición: New.

Hardback. Condición: New. The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data.* Consistent treatment from classical to modern Bayes * Underlying distribution theory to algorithm development * Many examples and applications * Does not assume statistical background * Extensive supporting appendixes * Accompanying lab manual in R.…

Condición: New. 2007. Hardcover. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful statistical tools for making sense of ecological processes. This work introduces them to these methods in modeling and computation. It also describes how complexity can motivate a shift to Bayesian methods. Num Pages: 632 pages, 163 line illus. 21 tables. BIC Classification: JHBC; RN. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 255 x 210 x 40. Weight in Grams: 1680. . . . . . Books ship from the US and Ireland.…

Condición: New. In English.

hardcover. Condición: New. In shrink wrap. Looks like an interesting title.

Hardback. Condición: New. The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data.* Consistent treatment from classical to modern Bayes * Underlying distribution theory to algorithm development * Many examples and applications * Does not assume statistical background * Extensive supporting appendixes * Accompanying lab manual in R.…

Hardcover. Condición: Brand New. illustrated edition. 632 pages. 10.00x8.00x1.50 inches. In Stock.

Hardcover. Condición: Brand New. illustrated edition. 632 pages. 10.00x8.00x1.50 inches. In Stock. This item is printed on demand.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful statistical tools for making sense of ecological processes. This work introduces them to these methods in modeling a.…

Hardcover. Condición: new. Hardcover. The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data.* Consistent treatment from classical to modern Bayes * Underlying distribution theory to algorithm development * Many examples and applications * Does not assume statistical background * Extensive supporting appendixes * Accompanying lab manual in R Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful statistical tools for making sense of ecological processes. This work introduces them to these methods in modeling and computation. It also describes how complexity can motivate a shift to Bayesian methods. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes.In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data.Consistent treatment from classical to modern BayesUnderlying distribution theory to algorithm developmentMany examples and applicationsDoes not assume statistical backgroundExtensive supporting appendixesLab manual in R is available separately.…