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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
Toward a ground-motion logic tree for probabilistic seismic hazard assessment in Europe
Elise Delavaud,Fabrice Cotton,Fabrice Cotton,Sinan Akkar,Frank Scherbaum,Laurentiu Danciu,Laurentiu Danciu,Laurentiu Danciu,Céline Beauval,Céline Beauval,Stéphane Drouet,Stéphane Drouet,John Douglas,Roberto Basili,M. Abdullah Sandıkkaya,Margaret Segou,Ezio Faccioli,Nikos Theodoulidis +17 more
TL;DR: In this article, a logic tree for ground-motion prediction in Europe has been constructed by combining expert judgment and data testing, and sensitivity analysis of the weights on the seismic hazard has been conducted, showing that once the GMPEs have been selected, the associated set of weights has a smaller influence on the hazard.
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A primer on model selection using the Akaike Information Criterion
TL;DR: Some procedures for model calibration and a criterion, the Akaike Information Criterion, of model selection based on experimental data are described and the use of a collection of model is motivated.
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Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale
TL;DR: In this article, the authors examined how landscape complexity and crop rotation intensity in the landscape at different spatial scales affect the flow and the stability of natural pest control services in barley fields using manipulation cage experiments.
Journal ArticleDOI
Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring
TL;DR: In this paper, a 20-ha forest dynamics plot in a species-rich subtropical forest was analyzed using ground-based stem-mapping data and topographic and edaphic variables.
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Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff
TL;DR: In this article, a maximum likelihood version (MLBMA) of BMA is applied to seven alternative variogram models of log air permeability data from single-hole pneumatic injection tests in six boreholes at the Apache Leap Research Site (ALRS) in central Arizona.