706122 Bayesian Methods for the Physical Sciences
Wintersemester 2016/2017 | Stand: 23.11.2016 | LV auf Merkliste setzenCourse Syllabus:
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Probability axioms. Computation of the posterior: analytical vs numerical sampling.
Upper limits. Initial discussion on the role of the prior. Importance of checking numerical convergence. A glimpse on sensitivity analysis.
•
Single parameters models. Combining information coming from multiple data. The prior
(and the Malmquist-like effect). Prior sensitivity. Two-parameters models. Joint probability contours. Comparison of the performances of state-of-the-art methods to measure a dispersion.
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Introduction to regression. Comparison of regression fitters. Regressions (of increasing difficulty): non-linear regression with non-gaussian errors of different sizes (but no er roron predictor and no intrinsic scatter). Allowing systematics (intrinsic scatter). Allowing errors on x. Regressions with two (or more) predictors. A glimpse on other important issues such as mixture of regressions, non-random data collection, model checking.
Course Syllabus:
•
Probability axioms. Computation of the posterior: analytical vs numerical sampling.
Upper limits. Initial discussion on the role of the prior. Importance of checking numerical convergence. A glimpse on sensitivity analysis.
•
Single parameters models. Combining information coming from multiple data. The prior
(and the Malmquist-like effect). Prior sensitivity. Two-parameters models. Joint probability contours. Comparison of the performances of state-of-the-art methods to measure a dispersion.
•
Introduction to regression. Comparison of regression fitters. Regressions (of increasing difficulty): non-linear regression with non-gaussian errors of different sizes (but no er roron predictor and no intrinsic scatter). Allowing systematics (intrinsic scatter). Allowing errors on x. Regressions with two (or more) predictors. A glimpse on other important issues such as mixture of regressions, non-random data collection, model checking.
- Fakultät für Mathematik, Informatik und Physik