From 44542e61f6fc693f7819df8408c5a6b82502187a Mon Sep 17 00:00:00 2001 From: Wolfgang Bangerth Date: Wed, 2 Feb 2022 16:02:14 -0700 Subject: [PATCH] Minor updates to the MCMC-Laplace benchmark readme file. --- MCMC-Laplace/Readme.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/MCMC-Laplace/Readme.md b/MCMC-Laplace/Readme.md index 094ef22..1593c25 100644 --- a/MCMC-Laplace/Readme.md +++ b/MCMC-Laplace/Readme.md @@ -2,8 +2,10 @@ Readme file for MCMC-Laplace ============================ @note The intent and implementation of this program is extensively - described in D. Aristoff and W. Bangerth: "to be completed upon - actually writing the paper". See there for more information. + described in D. Aristoff and W. Bangerth: "A benchmark for the Bayesian + inversion of coefficients in partial differential equations", + submitted, 2021. A preprint can be found + [here](https://arxiv.org/abs/2102.07263). See there for more information. Motivation for project ---------------------- @@ -23,8 +25,8 @@ which hypothesized make-up of the body would result in predicted reactions that are "closest" to the measured one. This formulation of the problem is what is now generally called the "deterministic inverse problem", and it is an optimization problem: Among all possible -make-ups of the body, which one minimizes the difference between -predicted measurements and actual measurements. +make-ups of the body, find the one which *minimizes* the difference between +predicted measurements and actual measurements. Since the late 1990s, a second paradigm for the formulation has come into play: "Bayesian inverse problems". It rests on the observation -- 2.39.5