CIRM - Videos & books Library - Bayesian data assimilation and filtering

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Authors : Schillings, Claudia (Author of the conference)
CIRM (Publisher )

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Abstract : We consider the inverse problem of recovering an unknown parameter from a finite set of indirect measurements. We start with reviewing the formulation of the Bayesian approach to inverse problems. In this approach the data and the unknown parameter are modelled as random variables; the distribution of the data is given and the unknown is assumed to be drawn from a given prior distribution. The solution, called the posterior distribution, is the probability distribution of the unknown given the data, obtained through the Bayes rule. We will talk about the conditions under which this formulation leads to well-posedness of the inverse problem at the level of probability distributions. We then discuss the connection of the Bayesian approach to inverse problems with the variational regularization. This will also help us to study the properties of the modes of the posterior distribution as point estimators for the unknown parameter. We will also briefly talk about the Markov chain Monte Carlo methods in this context.

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http://smai.emath.fr/cemracs/cemracs21/data/presentation-speakers/schillings2.pdf

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Hennenfent, Guillaume

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https://videos.cirm-math.fr/2021-07-21-Schillings_2.mp4

Information on the Event

Event Title : CEMRACS 2021: Data Assimilation and Model Reduction in High Dimensional Problems / CEMRACS 2021: Assimilation de données et réduction de modèle pour des problêmes en grande dimension
Event Organizers : Ehrlacher, Virginie ; Lombardi, Damiano ; Mula Hernandez, Olga ; Nobile, Fabio ; Taddei, Tommaso
Dates : 19/07/2021 - 23/07/2021
Event Year : 2021
Event URL : https://conferences.cirm-math.fr/2412.html

Citation Data

DOI : 10.24350/CIRM.V.19788703
Cite this video as: Schillings, Claudia (2021). Bayesian data assimilation and filtering - lecture 2. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19788703
URI : http://dx.doi.org/10.24350/CIRM.V.19788703

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