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Information-theoretic limits of Bayesian inference in Gaussian noise Miolane, Léo
Description
We will discuss briefly the statistical estimation of a signal (vector, matrix, tensor...) corrupted by Gaussian noise. We will restrict ourselves to information-theoretic considerations and draw connections with statistical physics (random energy model, p-spin model).
Item Metadata
| Title |
Information-theoretic limits of Bayesian inference in Gaussian noise
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| Creator | |
| Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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| Date Issued |
2020-07-09T11:32
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| Description |
We will discuss briefly the statistical estimation of a signal (vector, matrix, tensor...) corrupted by Gaussian noise. We will restrict ourselves to information-theoretic considerations and draw connections with statistical physics (random energy model, p-spin model).
|
| Extent |
49.0 minutes
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| Subject | |
| Type | |
| File Format |
video/mp4
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| Language |
eng
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| Notes |
Author affiliation: New York University
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| Series | |
| Date Available |
2021-01-06
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
| DOI |
10.14288/1.0395458
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| URI | |
| Affiliation | |
| Peer Review Status |
Unreviewed
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| Scholarly Level |
Researcher
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International