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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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PyMC: Bayesian Stochastic Modelling in Python.
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Ecology drives the worldwide distribution of human diseases.
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New paradigms for modelling species distributions
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Use of Maximum Entropy Modeling in Wildlife Research
TL;DR: Further advances are needed to better define model thresholds, to test model significance, and to address model selection to strengthen the utility of Maxent for wildlife research and management.