From 7c3f5d41a8f82b61cbc3246866938427160d0e81 Mon Sep 17 00:00:00 2001
From: Hu Zhao <zhao@mbd.rwth-aachen.de>
Date: Sat, 22 Apr 2023 05:07:01 +0200
Subject: [PATCH] docs: add ref for active learning
---
docs/source/refs.bib | 33 +++++++++++++++++++++++++++++++++
1 file changed, 33 insertions(+)
diff --git a/docs/source/refs.bib b/docs/source/refs.bib
index d997e61..c3bfd22 100644
--- a/docs/source/refs.bib
+++ b/docs/source/refs.bib
@@ -62,6 +62,16 @@
author = "Jon Herman and Will Usher"
}
+@article{Kandasamy2017,
+ title = "Query efficient posterior estimation in scientific experiments via {Bayesian} active learning",
+ journal = "Artificial Intelligence",
+ volume = "243",
+ pages = "45--56",
+ year = "2017",
+ doi = "https://doi.org/10.1016/j.artint.2016.11.002",
+ author = "Kirthevasan Kandasamy and Jeff Schneider and Barnab{\'a}s P{\'o}czos",
+}
+
@article{LeGratiet2014,
title = "A {Bayesian} approach for global sensitivity analysis of (multifidelity) computer codes",
journal = "SIAM/ASA Journal on Uncertainty Quantification",
@@ -106,6 +116,17 @@
author = "Andrea Saltelli and Paola Annoni and Ivano Azzini and Francesca Campolongo and Marco Ratto and Stefano Tarantola"
}
+@article{Wang2018,
+ title = "Adaptive {Gaussian} process approximation for {Bayesian} inference with expensive likelihood functions",
+ journal = "Neural Computation",
+ volume = "30",
+ number = "11",
+ pages = "3072--3094",
+ year = "2018",
+ doi = "10.1162/neco_a_01127",
+ author = "Hong Qiao Wang and Jing Lai Li"
+}
+
@article{Zhao2021a,
title = "Emulator-based global sensitivity analysis for flow-like landslide run-out models",
journal = "Landslides",
@@ -124,3 +145,15 @@
year = {2021},
doi = {10.18154/RWTH-2021-11693}
}
+
+@article{Zhao2022,
+ title = {Bayesian active learning for parameter calibration of landslide run-out models},
+ journal = {Landslides},
+ volume = {19},
+ number = {},
+ pages = {2033--2045},
+ year = {2022},
+ doi = {https://doi.org/10.1007/s10346-022-01857-z},
+ author = {Zhao, Hu and Kowalski, Julia}
+}
+
--
GitLab