Information Geometry for Approximate Bayesian Computation-Reference-Cited by-同舟云学术

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Information Geometry for Approximate Bayesian Computation

Published:2020-01 Issue:1 Volume:8 Page:229-260
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Spiliopoulos Konstantinos

Funder

National Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modelling and Simulation,Statistics and Probability

Link

https://epubs.siam.org/doi/pdf/10.1137/18M123284X

Reference20 articles.

1. Goal-oriented sensitivity analysis for lattice kinetic Monte Carlo simulations

2. Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics

3. The rate of convergence for approximate Bayesian computation

4. New insights into Approximate Bayesian Computation

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