Layered adaptive importance sampling
Sign Up to like & getrecommendations! Published in 2017 at "Statistics and Computing"
DOI: 10.1007/s11222-016-9642-5
Abstract: Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to… read more here.
Keywords: importance sampling; adaptive importance; monte carlo; importance ... See more keywords
Doubly Adaptive Importance Sampling
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DOI: 10.1080/10618600.2025.2530048
Abstract: We propose an adaptive importance sampling scheme for Gaussian approximations of intractable posteriors. Optimization-based approximations like variational inference can be too inaccurate while existing Monte Carlo methods can be too slow. Therefore, we propose a… read more here.
Keywords: importance; adaptive importance; doubly adaptive; variational inference ... See more keywords
Hamiltonian Adaptive Importance Sampling
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2021.3068616
Abstract: Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated from the so-called proposal distribution, and the choice… read more here.
Keywords: hamiltonian adaptive; methodology; importance; hais ... See more keywords