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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
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TLDR
The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).Abstract:
Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summaryread more
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Journal ArticleDOI
Can mechanism inform species’ distribution models?
Lauren B. Buckley,Mark C. Urban,Michael J. Angilletta,Lisa G. Crozier,Leslie J. Rissler,Michael W. Sears +5 more
TL;DR: It is compared how two correlative and three mechanistic models predicted the ranges of two species: a skipper butterfly and a fence lizard, to find out how these models performed similarly in predicting current distributions.
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Do species’ traits predict recent shifts at expanding range edges?
Amy L. Angert,Lisa G. Crozier,Leslie J. Rissler,Sarah E. Gilman,Joshua J. Tewksbury,Amanda J. Chunco +5 more
TL;DR: Current evidence for the relationship between leading-edge range shifts and species' traits is assessed and expected relationships for several datasets are found, including diet breadth in North American Passeriformes and egg-laying habitat in British Odonata are found.
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The Bayesian information criterion: background, derivation, and applications
TL;DR: The conceptual and theoretical foundations for the Bayesian information criterion are reviewed, and its properties and applications are discussed.
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Impacts of invasive alien marine species on ecosystem services and biodiversity: a pan-European review.
Stelios Katsanevakis,Inger Wallentinus,Argyro Zenetos,Erkki Leppäkoski,Melih Ertan Çinar,Bayram Öztürk,Michał Grabowski,Daniel Golani,Ana Cristina Cardoso +8 more
TL;DR: Kanevakis*, Inger Wallentinus, Argyro Zenetos, Erkki Leppakoski, Melih Ertan Cinar, Bayram Ozturk, Michal Grabowski, Daniel Golani and Ana Cristina Cardoso European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability (IES), Ispra, Italy Department of Biological and Environmental Sciences, University of Gothenburg, Sweden Institute of Marine Biological Resources and Inland Waters, Hellenic Centre for Marine Research, Ag.
PyMC: Bayesian Stochastic Modelling in Python
TL;DR: This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.