Rafael Polania: Katalogdaten im Frühjahrssemester 2021

NameHerr Dr. Rafael Polania
Adresse
Neurowiss. der Entscheid.prozesse
ETH Zürich, Y36 M 6
Winterthurerstrasse 190
8057 Zürich
SWITZERLAND
Telefon+41 44 633 99 75
E-Mailrafael.polania@hest.ethz.ch
DepartementGesundheitswissenschaften und Technologie
BeziehungAssistenzprofessor

NummerTitelECTSUmfangDozierende
376-1986-00LBayesian Data Analysis and Models of Behavior (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: DOEC0829

Beachten Sie die Einschreibungstermine an der UZH: Link
3 KP2SR. Polania, Uni-Dozierende
KurzbeschreibungMaking sense of the data acquired via experiments is fundamental in many fields of sciences. This course is designed for students/researchers who want to gain practical experience with data analysis based on Bayesian inference. Coursework involves practical demonstrations and discussion of solutions for data analysis problems. No advanced knowledge of statistics and probability is required.
LernzielThe overall goal of this course it that the students are able to develop both analytic and problem-solving skills that will serve to draw reasonable inferences from observations. The first objective is to make the participants familiar with the conceptual framework of Bayesian data analysis. The second goal is to introduce the ideas of modern Bayesian data analysis, including techniques such as Markov chain Monte Carlo (MCMC) techniques, alongside the introduction of programming tools that facilitate the creation of any Bayesian inference model. Throughout the course, this will involve practical demonstrations with example datasets, homework, and discussions that should convince the participants of this course that it is possible to make inference and understand the data acquired from the experiments that they usually obtain in their own research (starting from simple linear regressions all the way up to more complex models with hierarchical structures and dependencies). After working through this course, the participants should be able to build their own inference models in order to interpret meaningfully their own data.
Voraussetzungen / BesonderesThe very basics (or at least intuition) of programming in either Matlab or R