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Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE
TLDR
We describe NIMBLE, a system for programming statistical algorithms for general model structures within R, a language for programming algorithms that can use different models. Expand
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Model-averaged Wald confidence intervals
TLDR
We propose a new method for construction of a model-averaged Wald confidence interval, based on the idea of model averaging tail areas of the sampling distributions of the single-model estimates. Expand
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Efficient Markov chain Monte Carlo sampling for hierarchical hidden Markov models
TLDR
We study combinations of existing methods, which are shown to vastly improve computational efficiency for these hierarchical models while maintaining the modeling flexibility provided by embedded HMMs. Expand
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Distribution and abundance of Hector's dolphins off Otago, New Zealand
Abstract Data on the distribution and abundance of Hector's dolphins (Cephalorhynchus hectori) along the Otago coastline, between Taieri Mouth and Ōamaru (approximately 130 km alongshore), wereExpand
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Automated Parameter Blocking for Efficient Markov-Chain Monte Carlo Sampling
TLDR
We propose an automated procedure to determine an efficient MCMC algorithm for a given model and computing platform, and observe non-trivial improvements in MCMC efficiency. Expand
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Design of Efficient Digital Interpolation Filters for Integer Upsampling
TLDR
Digital signal interpolation systems for integer upsampling can be implemented in a variety of ways, and their computational costs are calculated. Expand
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IPM2: toward better understanding and forecasting of population dynamics
Dynamic population models typically aim to predict demography and the resulting population dynamics in relation to environmental variation. However, they rarely include the diversity of individualExpand
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Museum specimen data reveal emergence of a plant disease may be linked to increases in the insect vector population.
The emergence rate of new plant diseases is increasing due to novel introductions, climate change, and changes in vector populations, posing risks to agricultural sustainability. Assessing andExpand
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Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals
Model averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged BayesianExpand
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