$\vec{w}h\alpha\mathfrak{t}\;\; i\mathbb{S}\ldots$

Uncertainty Quantification?

Phillipp Wacker (FAU Erlangen-N├╝rnberg)
2022/02/22, 09:15
Before the BMS Days' talk by Prof. Claudia Schillings
Due to the current situation, the talk takes place online, via zoom. The meeting link has been sent out via the usual mailing lists; please contact the organisers if you have not received the email and would like to join the talk.
About what?

Most of us in principle know how to solve an equation for a parameter $x$. Either there is an analytical solution or, if all else fails, we can throw some numerics (for example, Newton's algorithm) at it in order to obtain an approximation to it. But what if we additionally have some prior belief about the parameter, for example an expert's opinion? How can we mathematically merge a-priori information with acquired data in order to gain insights about hidden parameters? We will talk about how Bayes' Theorem allows us to do just that and how this makes it possible to also quantify uncertainty in our inferred parameter's value.
Slides are available here.