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11/03/2020
Bayesian Workflow
The Bayesian approach to data analysis provides a powerful way to handle...
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09/30/2020
Using sex and gender in survey adjustment
Accounting for sex and gender characteristics is a complex, structural c...
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04/27/2020
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation
Gaussian latent variable models are a key class of Bayesian hierarchical...
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04/27/2020
Hamiltonian Monte Carlo using an embedded Laplace approximation
Latent Gaussian models are a popular class of hierarchical models with a...
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08/19/2019
Improving multilevel regression and poststratification with structured priors
A central theme in the field of survey statistics is estimating populati...
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07/09/2019
The Integrated nested Laplace approximation for fitting models with multivariate response
This paper introduces a Laplace approximation to Bayesian inference in r...
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06/28/2019
Data integration for high-resolution, continental-scale estimation of air pollution concentrations
Air pollution constitutes the highest environmental risk factor in relat...
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05/24/2019
The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
Cognitive modelling shares many features with statistical modelling, mak...
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03/19/2019
Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC
Markov chain Monte Carlo is a key computational tool in Bayesian statist...
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04/18/2018
Validating Bayesian Inference Algorithms with Simulation-Based Calibration
Verifying the correctness of Bayesian computation is challenging. This i...
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02/18/2018
Spatial modelling with R-INLA: A review
Coming up with Bayesian models for spatial data is easy, but performing ...
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02/07/2018
Yes, but Did It Work?: Evaluating Variational Inference
While it's always possible to compute a variational approximation to a p...
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06/17/2013
