From d16251443835fa67c3d6a010c80fea77fcaac0a8 Mon Sep 17 00:00:00 2001 From: Martin Fitzner Date: Fri, 19 Jul 2024 15:21:44 +0200 Subject: [PATCH] Remove repetition --- docs/userguide/active_learning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/userguide/active_learning.md b/docs/userguide/active_learning.md index 439927170..06ce8c1d2 100644 --- a/docs/userguide/active_learning.md +++ b/docs/userguide/active_learning.md @@ -10,7 +10,7 @@ above-mentioned criterion and set up a Bayesian optimization campaign to recomme points with the highest uncertainty, we achieve active learning via Bayesian optimization. In practice, this is procedure is implemented by setting up a probabilistic model of our measurement process that allows us to quantify uncertainty -in the form of a posterior posterior distribution, from which we can then construct an +in the form of a posterior distribution, from which we can then construct an uncertainty-based acquisition function to guide the exploration process. Below you find which acquisition functions in BayBE are suitable for this endeavor,