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Description
Zoom room for seminars: https://cern.zoom.us/j/98750947196?pwd=N1RTS3EzazBha0tURVJOcEZKREFxZz09
Meeting ID: 987 5094 7196
Passcode: 613113
Abstract:
From time to time, it is helpful to look over the shoulders of colleagues in other fields; we may be inspired to think about a problem in our own field a bit differently. It is also helpful, occasionally, to come up for air and remind ourselves of a few of the core statistical ideas that transcend fields. In this talk, I begin with the simple, but highly instructive, ON/OFF problem in astronomy in order to highlight similarities and differences between the frequentist and Bayesian viewpoints. Then, I consider the fitting of cosmological models to Type Ia supernovae data in order to illustrate the concept of parameter non-identifiability, that is, the ambiguities that can arise in fitting models to data. In view of the explosive growth of interest in machine learning, I end with a few remarks about machine learning models and algorithms.
Meeting ID: 987 5094 7196
Passcode: 613113
Abstract:
From time to time, it is helpful to look over the shoulders of colleagues in other fields; we may be inspired to think about a problem in our own field a bit differently. It is also helpful, occasionally, to come up for air and remind ourselves of a few of the core statistical ideas that transcend fields. In this talk, I begin with the simple, but highly instructive, ON/OFF problem in astronomy in order to highlight similarities and differences between the frequentist and Bayesian viewpoints. Then, I consider the fitting of cosmological models to Type Ia supernovae data in order to illustrate the concept of parameter non-identifiability, that is, the ambiguities that can arise in fitting models to data. In view of the explosive growth of interest in machine learning, I end with a few remarks about machine learning models and algorithms.