Formalizes pre-data effective sample size for GGMs under Wishart and G-Wishart priors and introduces DPIR and BFDA extensions for sample size planning.
and Mõttus, René and Borsboom, Denny , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
citing papers explorer
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What is your Prior Worth? Effective Sample Size and Sample Size Planning for Gaussian Graphical Models
Formalizes pre-data effective sample size for GGMs under Wishart and G-Wishart priors and introduces DPIR and BFDA extensions for sample size planning.
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Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.