Exploiting Model Hierarchies, Sparsity and Low-rank Structure of Large-scale Bayesian Computation - Part II of II | SIAM

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2019 SIAM Conference on Com...
Exploiting Model Hierarchie...

Exploiting Model Hierarchies, Sparsity and Low-rank Structure of Large-scale Bayesian Computation - Part II of II

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CSE19 - MS204-1: Consistent Parameter Distributions in High-dimensional Spaces: Built One Dimension at a Time

Presentation: Troy Butler, University of Colorado, Denver, USA, 23 min 33 sec

CSE19 - MS204-1: Consistent Parameter Distributions in High-dimensional Spaces: Built One Dimension at a Time

Document: CSE19 - MS204-1: Consistent Parameter Distributions in High-dimensional Spaces: Built One Dimension at a Time

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CSE19 - MS204-2: Low-rank Structure in Optimization-based Sampling

Presentation: Zheng Wang, Massachusetts Institute of Technology, U.S., 19 min 50 sec

CSE19 - MS204-2: Low-rank Structure in Optimization-based Sampling

Document: CSE19 - MS204-2: Low-rank Structure in Optimization-based Sampling

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CSE19 - MS204-3: Mitigating the Cost of PDE-constrained Bayesian Inverse Problems Using Dimensionality Reduction and Machine Learning

Presentation: Sheroze Sheriffdeen, Institute for Computational Engineering and Sciences, U.S., 18 min 35 sec

CSE19 - MS204-3: Mitigating the Cost of PDE-constrained Bayesian Inverse Problems Using Dimensionality Reduction and Machine Learning

Document: CSE19 - MS204-3: Mitigating the Cost of PDE-constrained Bayesian Inverse Problems Using Dimensionality Reduction and Machine Learning

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CSE19 - MS204-4: Hierarchical Off-diagonal Low-rank Approximation for Hessians in Bayesian Inference

Presentation: Tucker Hartland, University of California, Merced, U.S., 19 min 4 sec

CSE19 - MS204-4: Hierarchical Off-diagonal Low-rank Approximation for Hessians in Bayesian Inference

Document: CSE19 - MS204-4: Hierarchical Off-diagonal Low-rank Approximation for Hessians in Bayesian Inference