A01=Mevin B. Hooten
A01=N. Thompson Hobbs
Accuracy and precision
Additive model
Age Group_Uncategorized
Age Group_Uncategorized
Author_Mevin B. Hooten
Author_N. Thompson Hobbs
automatic-update
Bayes factor
Bayesian
Bayesian inference
Bayesian information criterion
Bayesian statistics
Beta distribution
Bias of an estimator
Category1=Non-Fiction
Category=PBT
Category=PBWH
Category=PSAF
Central limit theorem
Chi-squared test
Conjugate prior
COP=United States
Count data
Cross-validation (statistics)
Delivery_Delivery within 10-20 working days
Deviance information criterion
Diagram (category theory)
Dummy variable (statistics)
Ensemble learning
eq_bestseller
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Equation
Estimation
Free parameter
Generalized linear model
Gibbs sampling
Hyperparameter
Identifiability
Inference
Inverse-gamma distribution
Iteration
Jeffreys prior
Joint probability distribution
Kernel density estimation
Language_English
Latent variable
Likelihood function
Likelihood-ratio test
Linear regression
Loss function
Marginal distribution
Markov chain Monte Carlo
Meta-analysis
Metropolis-Hastings algorithm
Model checking
Model selection
Monte Carlo algorithm
Normal distribution
Observational study
Overdispersion
PA=Available
Parameter
Posterior predictive distribution
Posterior probability
Prediction
Predictive inference
Price_€50 to €100
Prior probability
Probability
Probability density function
Probability distribution
Probability mass function
PS=Active
Quantile
Quantity
Random variable
Ranking (information retrieval)
Simple linear regression
softlaunch
Stationary distribution
Statistical power
Stochastic
Student's t-test
Summary statistics
Test statistic
Tikhonov regularization
Uncertainty
Variance
Product details
- ISBN 9780691159287
- Weight: 680g
- Dimensions: 152 x 235mm
- Publication Date: 04 Aug 2015
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
- Language: English
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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
N. Thompson Hobbs is senior research scientist at the Natural Resource Ecology Laboratory and professor in the Department of Ecosystem Science and Sustainability at Colorado State University. Mevin B. Hooten is associate professor in the Department of Fish, Wildlife, and Conservation Biology and the Department of Statistics at Colorado State University, and assistant unit leader in the US Geological Survey's Colorado Cooperative Fish and Wildlife Research Unit.
