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pro vyhledávání: '"de Valpine P"'
Autor:
Paganin, Sally, de Valpine, Perry
Posterior predictive p-values (ppps) have become popular tools for Bayesian model assessment, being general-purpose and easy to use. However, interpretation can be difficult because their distribution is not uniform under the hypothesis that the mode
Externí odkaz:
http://arxiv.org/abs/2306.04866
Autor:
Paganin, Sally, Paciorek, Christopher J., Wehrhahn, Claudia, Rodriguez, Abel, Rabe-Hesketh, Sophia, de Valpine, Perry
Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures offer a more flexible representation
Externí odkaz:
http://arxiv.org/abs/2101.11583
Autor:
Campbell, Harlan, de Valpine, Perry, Maxwell, Lauren, de Jong, Valentijn MT, Debray, Thomas, Jänisch, Thomas, Gustafson, Paul
A key challenge in estimating the infection fatality rate (IFR) -- and its relation with various factors of interest -- is determining the total number of cases. The total number of cases is not known because not everyone is tested, but also, more im
Externí odkaz:
http://arxiv.org/abs/2005.08459
Akademický článek
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Akademický článek
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Autor:
Nguyen, Dao, de Valpine, Perry, Atchade, Yves, Turek, Daniel, Michaud, Nicholas, Paciorek, Christopher
Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open question. This paper introduces a novel MCMC algorithm, namely, Auto Adapt MC
Externí odkaz:
http://arxiv.org/abs/1802.08798
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierar
Externí odkaz:
https://doaj.org/article/e4fcfb9f6af344b19158af8e7031e246
nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms. Specifically, th
Externí odkaz:
http://arxiv.org/abs/1703.06206
Publikováno v:
Journal of Statistical Software, Vol 100, Pp 1-39 (2021)
nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms. Specifically, th
Externí odkaz:
https://doaj.org/article/7903ca0e750e4f20a8ad6b7a3ac1f6a3
Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variabl
Externí odkaz:
http://arxiv.org/abs/1601.02698