Introduces penalized spline SCR models fitted via Laplace-approximate penalized marginal likelihood to flexibly model nonlinear covariate effects on density and approximate LGCP activity centre processes.
TMB: Automatic differentiation and Laplace approximation
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ADELIA is the first AD-enabled INLA system that computes exact hyperparameter gradients via a structure-exploiting multi-GPU backward pass, delivering 4.2-7.9x per-gradient speedups and 5-8x better energy efficiency than finite differences on models with up to 1.9 million latent variables.
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.
citing papers explorer
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Spatial Capture-Recapture With Penalized Regression Splines to Flexibly Model Wildlife Density and Distribution
Introduces penalized spline SCR models fitted via Laplace-approximate penalized marginal likelihood to flexibly model nonlinear covariate effects on density and approximate LGCP activity centre processes.
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ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
ADELIA is the first AD-enabled INLA system that computes exact hyperparameter gradients via a structure-exploiting multi-GPU backward pass, delivering 4.2-7.9x per-gradient speedups and 5-8x better energy efficiency than finite differences on models with up to 1.9 million latent variables.
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Online forecast reconciliation using linear models
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.