عنوان

Bayesian models :

شابک

1400866553

شابک

9781400866557

شابک اشتباه

0691159289

شابک اشتباه

9780691159287

عنوان اصلي

Bayesian models :

نام عام مواد

[Book]

ساير اطلاعات عنواني

a statistical primer for ecologists /

نام نخستين پديدآور

N. Thompson Hobbs and Mevin B. Hooten.

محل نشرو پخش و غیره

Princeton :

نام ناشر، پخش کننده و غيره

Princeton University Press,

تاریخ نشرو بخش و غیره

2015.

تاریخ نشرو بخش و غیره

©2015

نام خاص و کميت اثر

1 online resource (xiv, 300 pages)

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

Cover; Title; Copyright; Contents; Preface; I Fundamentals; 1 PREVIEW; 1.1 A Line of Inference for Ecology; 1.2 An Example Hierarchical Model; 1.3 What Lies Ahead?; 2 DETERMINISTIC MODELS; 2.1 Modeling Styles in Ecology; 2.2 A Few Good Functions; 3 PRINCIPLES OF PROBABILITY; 3.1 Why Bother with First Principles?; 3.2 Rules of Probability; 3.3 Factoring Joint Probabilities; 3.4 Probability Distributions; 4 LIKELIHOOD; 4.1 Likelihood Functions; 4.2 Likelihood Profiles; 4.3 Maximum Likelihood; 4.4 The Use of Prior Information in Maximum Likelihood; 5 SIMPLE BAYESIAN MODELS; 5.1 Bayes' Theorem.

متن يادداشت

12.1 Fisher's Ticks12.2 Light Limitation of Trees; 12.3 Landscape Occupancy of Swiss Breeding Birds; 12.4 Allometry of Savanna Trees; 12.5 Movement of Seals in the North Atlantic; Afterword; Acknowledgments; A Probability Distributions and Conjugate Priors; Bibliography; Index.

متن يادداشت

5.2 The Relationship between Likelihood and Bayes'5.3 Finding the Posterior Distribution in Closed Form; 5.4 More about Prior Distributions; 6 HIERARCHICAL BAYESIAN MODELS; 6.1 What Is a Hierarchical Model?; 6.2 Example Hierarchical Models; 6.3 When Are Observation and Process Variance Identifiable?; II Implementation; 7 MARKOV CHAIN MONTE CARLO; 7.1 Overview; 7.2 How Does MCMC Work?; 7.3 Specifics of the MCMC Algorithm; 7.4 MCMC in Practice; 8 INFERENCE FROM A SINGLE MODEL; 8.1 Model Checking; 8.2 Marginal Posterior Distributions; 8.3 Derived Quantities.

متن يادداشت

8.4 Predictions of Unobserved Quantities8.5 Return to the Wildebeest; 9 INFERENCE FROM MULTIPLE MODELS; 9.1 Model Selection; 9.2 Model Probabilities and Model Averaging; 9.3 Which Method to Use?; III Practice in Model Building; 10 WRITING BAYESIAN MODELS; 10.1 A General Approach; 10.2 An Example of Model Building: Aboveground Net Primary Production in Sagebrush Steppe; 11 PROBLEMS; 11.1 Fisher's Ticks; 11.2 Light Limitation of Trees; 11.3 Landscape Occupancy of Swiss Breeding Birds; 11.4 Allometry of Savanna Trees; 11.5 Movement of Seals in the North Atlantic; 12 SOLUTIONS.

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

"Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models."

منبع سفارش / آدرس اشتراک

JSTOR

منبع سفارش / آدرس اشتراک

MIL

شماره انبار

22573/ctt1dr30zr

شماره انبار

803585

عنوان

Bayesian models.

شماره استاندارد بين المللي کتاب و موسيقي

9780691159287

موضوع مستند نشده

Bayesian statistical decision theory.

موضوع مستند نشده

Ecology-- Statistical methods.

موضوع مستند نشده

Bayesian statistical decision theory.

موضوع مستند نشده

Ecology-- Statistical methods.

موضوع مستند نشده

MATHEMATICS-- Calculus.

موضوع مستند نشده

MATHEMATICS-- Mathematical Analysis.

موضوع مستند نشده

SCIENCE-- Life Sciences-- Ecology.

موضوع مستند نشده

MAT-- 005000

موضوع مستند نشده

MAT-- 034000

موضوع مستند نشده

MAT029010

موضوع مستند نشده

SCI020000

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Hobbs, N. Thompson

مستند نام اشخاص تاييد نشده

Hooten, Mevin B.,1976-

تاريخ عمليات

20200822122841.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y