Bayesian Models
Mevin B. Hooten, N. Thompson Hobbs
Naturwissenschaften, Medizin, Informatik, Technik / ÷kologie
Beschreibung
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
Kundenbewertungen
Additive model, Model selection, Tikhonov regularization, Markov chain Monte Carlo, Uncertainty, Chi-squared test, Probability mass function, Ensemble learning, Bayesian, Summary statistics, Kernel density estimation, Normal distribution, Probability distribution, Probability, Bayesian information criterion, Bayesian statistics, Marginal distribution, Generalized linear model, Probability density function, Test statistic, Deviance information criterion, Simple linear regression, Variance, Equation, Free parameter, Stochastic, Hyperparameter, Count data, Prior probability, Linear regression, Identifiability, Monte Carlo algorithm, Bias of an estimator, Accuracy and precision, Statistical power, Parameter, Predictive inference, Meta-analysis, Inference, Stationary distribution, Jeffreys prior, Metropolis–Hastings algorithm, Likelihood-ratio test, Student's t-test, Model checking, Cross-validation (statistics), Diagram (category theory), Inverse-gamma distribution, Likelihood function, Quantile, Latent variable, Bayes factor, Quantity, Estimation, Central limit theorem, Loss function, Posterior probability, Bayesian inference, Conjugate prior, Prediction, Ranking (information retrieval), Iteration, Observational study, Gibbs sampling, Overdispersion, Dummy variable (statistics), Beta distribution, Joint probability distribution, Posterior predictive distribution, Random variable