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Tutorials | University of Tübingen
Frank Jäkel University of Osnabrück. The first part of the course is a basic introduction to probability theory from a Bayesian perspective, covering conditional probability, independence, Bayes' rule, coherence, calibration, expectation, and decision-making. We will also discuss how Bayesian inference differs from frequentist inference. In the second part of the course we will discuss why Bayesian Decision Theory provides a good starting point for probabilistic models of perception and cognition. The focus here will be on rational analysis and ideal observer models that provide an analysis of the task, the environment, the background assumptions and the limitations of the cognitive system under study.
11. Introduction to Bayesian Analysis of Hydrologic Variables
Springer Professional. Back to the search result list. Table of Contents. Hint Swipe to navigate through the chapters of this book Close hint. Abstract In Sect.
Nadine Berner. Home Search Browse Submit Sitemap. Deciphering multiple changes in complex climate time series using Bayesian inference Bayes'sche Inferenz als diagnostischer Ansatz zur Untersuchung multipler Übergänge in komplexen Klimazeitreihen. Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of the observations. A precise detection of multiple changes is therefore of great importance for various research disciplines, such as environmental sciences, bioinformatics and economics.