Laplace approximation

Approximate Bayesian inference for structural equation models

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B9 R2325

This talk reviews a fast approximate Bayesian SEM method that combines Laplace, variational Bayes, and Gaussian copula techniques to deliver near-MLE speed with MCMC-like inference, and is implemented in the R package INLAvaan.

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo By Chaouki ben Issaid (Master Student of Prof. Raul Tempone, KAUST)

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B1 R4214

Experimental design can be vital when experiments are resource exhaustive and time-consuming. In this work, we carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions.

Bayesian OED for core-flooding experiment application based on Laplace Approximation by Longting Mo (Visiting Master student, Nanjing University, China)

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B1 R4214

Core flooding experiments are always needed to be conducted before the application of Enhanced Oil Production (EOR) in the field, which is a great way to improve the oil recovery. In order to optimize existing resources and obtain the information efficiently, it is necessary to optimize these experiments.