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

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CSE19 - MS170-2: Layers of Low-rank Couplings for Large-scale Bayesian Inference

Presentation: Daniele Bigoni, Massachusetts Institute of Technology, U.S., 24 min 29 sec

CSE19 - MS170-2: Layers of Low-rank Couplings for Large-scale Bayesian Inference

Document: CSE19 - MS170-2: Layers of Low-rank Couplings for Large-scale Bayesian Inference

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CSE19 - MS170-3: Multi-Index and Multi-level Markov Chain Monte Carlo in MUQ2

Presentation: Linus Seelinger, Universität Heidelberg, Germany, 18 min 47 sec

CSE19 - MS170-3: Multi-Index and Multi-level Markov Chain Monte Carlo in MUQ2

Document: CSE19 - MS170-3: Multi-Index and Multi-level Markov Chain Monte Carlo in MUQ2

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CSE19 - MS170-4: Use of the Bayesian Approximation Error Approach to Account for Model Discrepancy: The Robin Problem Revisited

Presentation: Ruanui Nicholson, University of Auckland, New Zealand, 20 min 41 sec

CSE19 - MS170-4: Use of the Bayesian Approximation Error Approach to Account for Model Discrepancy: The Robin Problem Revisited

Document: CSE19 - MS170-4: Use of the Bayesian Approximation Error Approach to Account for Model Discrepancy: The Robin Problem Revisited