Minor rephrasing

Co-Authored-By: Lucas Rademaker <44430780+lr4d@users.noreply.github.com>

Author: sebastian-neubauer-by

_posts/2020-01-26-Pycon-de-writeup.markdown

@@ -119,7 +119,7 @@ Link: [https://www.youtube.com/watch?v=LCDIqL-8bHs](https://www.youtube.com/watc
 
 Speaker: Florian Wilhelm
 
-Summary: The talk introduced the concepts of aleatoric and epistemic uncertainty. It compared various methods for uncertainty estimates according to several categories, such as performance, implementation effort etc. A simple, one variable toy dataset was used to evaluate these methods in practice. Some methods apparently showed a poor performance such as Monte-Carlo dropouts. I personally would have like to learn on why some methods performed better or worse on the dataset or not and how this generalizes to real-world datasets. However, based on later conversations with the speaker, this seems a tough problem for some of the methods used. What I definitely learned was how to give an easy explanation on the difference between aleatoric and epistemic uncertainty, and on quantile regression to a broad audience. And it was the first time I had been given such a systematic overview on uncertainty quantification.
+Summary: The talk introduced the concepts of aleatoric and epistemic uncertainty. It compared various methods for uncertainty estimates according to several categories, such as performance, implementation effort etc. A simple, one variable toy dataset was used to evaluate these methods in practice. Some methods apparently showed a poor performance such as Monte-Carlo dropouts. I personally would have like to learn on why some methods performed better or worse on the dataset or not and how this generalizes to real-world datasets. However, based on later conversations with the speaker, this seems to be a tough problem for some of the methods used. What I definitely learned was how to give an easy explanation on the difference between aleatoric and epistemic uncertainty, and on quantile regression to a broad audience. And it was the first time I had been given such a systematic overview on uncertainty quantification.
 
 ### Title: Time series modelling with probabilistic programming