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Why environmental scientists are becoming Bayesians
Author(s) -
Clark James S.
Publication year - 2005
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/j.1461-0248.2004.00702.x
Subject(s) - inference , latent variable , bayes' theorem , econometrics , statistical inference , bayesian inference , computer science , allowance (engineering) , sample (material) , ecology , mathematics , machine learning , bayesian probability , statistics , artificial intelligence , biology , economics , operations management , chemistry , chromatography
Advances in computational statistics provide a general framework for the high‐dimensional models typically needed for ecological inference and prediction. Hierarchical Bayes (HB) represents a modelling structure with capacity to exploit diverse sources of information, to accommodate influences that are unknown (or unknowable), and to draw inference on large numbers of latent variables and parameters that describe complex relationships. Here I summarize the structure of HB and provide examples for common spatiotemporal problems. The flexible framework means that parameters, variables and latent variables can represent broader classes of model elements than are treated in traditional models. Inference and prediction depend on two types of stochasticity, including (1) uncertainty , which describes our knowledge of fixed quantities, it applies to all ‘unobservables’ (latent variables and parameters), and it declines asymptotically with sample size, and (2) variability , which applies to fluctuations that are not explained by deterministic processes and does not decline asymptotically with sample size. Examples demonstrate how different sources of stochasticity impact inference and prediction and how allowance for stochastic influences can guide research.