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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 * Summary

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Bayesian Phylogenetic Analysis of Combined Data

TL;DR: A Bayesian MCMC approach to the analysis of combined data sets was developed and its utility in inferring relationships among gall wasps based on data from morphology and four genes was explored, supporting the utility of morphological data in multigene analyses.
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Understanding predictive information criteria for Bayesian models

TL;DR: The Akaike, deviance, and Watanabe-Akaike information criteria are reviewed from a Bayesian perspective and it is better understood, through small examples, how these methods can apply in practice.
Journal ArticleDOI

Five (or so) challenges for species distribution modelling

TL;DR: Five areas of enquiry are identified and discussed that are of high importance for species distribution modelling: clarification of the niche concept; improved designs for sampling data for building models; improved parameterization; improved model selection and predictor contribution; and improved model evaluation.