- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- BIRS Workshop Lecture Videos /
- Bayesian optimal experimental design using Laplace-based...
Open Collections
BIRS Workshop Lecture Videos
BIRS Workshop Lecture Videos
Bayesian optimal experimental design using Laplace-based importance sampling Beck, Joakim
Description
In this talk, the focus is on optimizing strategies for the efficient computation of the inner loop of the classical double-loop Monte Carlo for Bayesian optimal experimental design. We propose the use of the Laplace approximation as an effective means of importance sampling, leading to a substantial reduction in computational work. This approach also efficiently mitigates the risk of numerical underflow. Optimal values for the method parameters are derived, where the average computational cost is minimized subject to a desired error tolerance. We demonstrate the computational efficiency of our method, as well as for a more recent approach that approximates using the Laplace method the return value of the inner loop. Finally, we present a set of numerical examples showing the efficiency of our method. The first example is a scalar problem that is linear in the uncertain parameter. The second example is a nonlinear scalar problem. The last example deals with sensor placements in electrical impedance tomography to recover the fiber orientation in laminate composites.
Item Metadata
Title |
Bayesian optimal experimental design using Laplace-based importance sampling
|
Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
|
Date Issued |
2017-10-12T11:39
|
Description |
In this talk, the focus is on optimizing strategies for the efficient computation of the inner loop of the classical double-loop Monte Carlo for Bayesian optimal experimental design. We propose the use of the Laplace approximation as an effective means of importance sampling, leading to a substantial reduction in computational work. This approach also efficiently mitigates the risk of numerical underflow. Optimal values for the method parameters are derived, where the average computational cost is minimized subject to a desired error tolerance. We demonstrate the computational efficiency of our method, as well as for a more recent approach that approximates using the Laplace method the return value of the inner loop. Finally, we present a set of numerical examples showing the efficiency of our method. The first example is a scalar problem that is linear in the uncertain parameter. The second example is a nonlinear scalar problem. The last example deals with sensor placements in electrical impedance tomography to recover the fiber orientation in laminate composites.
|
Extent |
15 minutes
|
Subject | |
Type | |
File Format |
video/mp4
|
Language |
eng
|
Notes |
Author affiliation: King Abdullah University of Science and Technology
|
Series | |
Date Available |
2018-04-11
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
DOI |
10.14288/1.0365323
|
URI | |
Affiliation | |
Peer Review Status |
Unreviewed
|
Scholarly Level |
Postdoctoral
|
Rights URI | |
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International