Implicitly adaptive importance sampling

DOI10.1007/s11222-020-09982-2zbMath1475.62053arXiv1906.08850OpenAlexW3022123134MaRDI QIDQ2058716

Paul-Christian Bürkner, Juho Piironen, Topi Paananen, Aki Vehtari

Publication date: 9 December 2021

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.08850

zbMATH Keywords

Monte CarloBayesian computationadaptive importance samplingleave-one-out cross-validation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Monte Carlo methods (65C05)

Related Items (4)

Projective inference in high-dimensional problems: prediction and feature selection ⋮ Gradient-based adaptive importance samplers ⋮ Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions ⋮ Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy

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