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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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Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems
TL;DR: An overview of Markov chain Monte Carlo, a sampling method for model inference and uncertainty quantification, is presented, which focuses on the Bayesian approach to MCMC, which allows us to estimate the posterior distribution of model parameters, without needing to know the normalising constant in Bayes' theorem.
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Individual variation in reproductive costs of reproduction: high‐quality females always do better
TL;DR: Offspring survival was lower in bighorn ewes following years of successful breeding than after years when no lamb was produced, suggesting that a cost of reproduction only occurred for low-quality females.
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Does stakeholder involvement really benefit biodiversity conservation
Juliette Young,Andrew Jordan,Kate R. Searle,Adam Butler,Daniel S. Chapman,Peter Simmons,Allan D. Watt +6 more
TL;DR: In this paper, the authors investigated the relationship between the characteristics of the process of stakeholder involvement and stakeholders' perceptions of future biodiversity outcomes, and found that increased actor involvement in the management of protected areas did not contribute to the conservation of biodiversity.
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An investigation of model selection criteria for neural network time series forecasting
Min Qi,Guoqiang Peter Zhang +1 more
TL;DR: Results indicate that the in-sample model selection criteria investigated are not able to provide a reliable guide to out-of-sample performance and there is no apparent connection between in- sample model fit and out- of-sample forecasting performance.
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Two-species occupancy models: a new parameterization applied to co-occurrence of secretive rails.
TL;DR: This work presents a new parameterization that is stable when covariates are included: the conditional two-species occupancy model, which can be used to examine alternative hypotheses for species' distribution patterns, and finds that Black Rail detection probability was unaffected by the detection of Virginia Rails, while, surprisingly, Black and Virginia Rail occupancy were positively associated even in small marshes.