[PDF][PDF] Distilling importance sampling

D Prangle - arXiv preprint arXiv:1910.03632, 2019 - academia.edu
Many complicated Bayesian posteriors are difficult to approximate by either sampling or
optimisation methods. Therefore we propose a novel approach combining features of both …

NuZZ: numerical Zig-Zag sampling for general models

F Pagani, A Chevallier, S Power, T House… - arXiv preprint arXiv …, 2020 - arxiv.org
Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics, and as
datasets grow larger and models grow more complex, many popular MCMC algorithms …

NuZZ: Numerical Zig-Zag for general models

F Pagani, A Chevallier, S Power, T House… - Statistics and …, 2024 - Springer
Abstract Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics,
and as datasets grow larger and models grow more complex, many popular MCMC …

Distilling importance sampling for likelihood free inference

D Prangle, C Viscardi - Journal of Computational and Graphical …, 2023 - Taylor & Francis
Likelihood-free inference involves inferring parameter values given observed data and a
simulator model. The simulator is computer code which takes parameters, performs …

[图书][B] ZigZag-based Markov chain Monte Carlo for sampling from challenging posterior distributions

F Pagani - 2021 - search.proquest.com
Abstract Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics,
allowing for a large amount of versatility in both model and data. However, as datasets grow …

[PDF][PDF] A Complete Bibliography of Publications in SIAM slash ASA Journal on Uncertainty Quantification

NHF Beebe - 2024 - 155.101.98.136
CHKP19, DMS23, Dia23, DY21, EFF+23, ESS15, EDA23, GP18, GBC+14, GGS23, GG24,
GP24, GK19, HS21, HSAY20, HQS21, HN17, Hos17, HKZ18a, HKZ18b, KLLU19, KBS13 …