Information Geometry for Approximate Bayesian Computation

DOI10.1137/18M123284XzbMath1436.62033arXiv1812.02127OpenAlexW3005174386MaRDI QIDQ4960991

Konstantinos V. Spiliopoulos

Publication date: 24 April 2020

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.02127

zbMATH Keywords

relative entropyinformation theoryBayesianABCuncertainty quantification

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Bayesian inference (62F15) Monte Carlo methods (65C05) Applications of renewal theory (reliability, demand theory, etc.) (60K10) Information geometry (statistical aspects) (62B11)

Uses Software

• GPS-ABC

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A tutorial on approximate Bayesian computation
• Approximate Bayesian computational methods
• Scalable information inequalities for uncertainty quantification
• The rate of convergence for approximate Bayesian computation
• New insights into approximate Bayesian computation
• Path-Space Information Bounds for Uncertainty Quantification and Sensitivity Analysis of Stochastic Dynamics
• Integrating Approximate Bayesian Computation with Complex Agent-Based Models for Cancer Research
• HIV with contact tracing: a case study in approximate Bayesian computation
• Parallelization, Processor Communication and Error Analysis in Lattice Kinetic Monte Carlo
• Inference for Stereological Extremes

This page was built for publication: Information Geometry for Approximate Bayesian Computation