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Statistical Inversion of Electromagnetic Logging DataAccelerated Bayesian Inversion Using Parallel Tempering

Statistical Inversion of Electromagnetic Logging Data: Accelerated Bayesian Inversion Using... [Applying tMCMC resolves two major problems we post at the beginning. One is the local minima problem and the other is a model selection problem. However, the observation tells us an inadequate performance when sampling a complex model. The decreased sampling efficiency is due to the dimensional changes. Hence, one possible solution comes to make MCMC methods more scalable and to be deployed on a high-performance computing system. The idea brings people to look for a parallel version of MCMC algorithms. In this chapter, we will introduce such a parallel schematic, which can be combined with any type of MCMC sampling method. We will first introduce the fundamental concept of tempering. And then we use the combination of tMCMC with parallel tempering to form up a complete workflow for solving a fast statistical inverse problem.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Statistical Inversion of Electromagnetic Logging DataAccelerated Bayesian Inversion Using Parallel Tempering

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References (7)

Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-57096-5
Pages
61 –78
DOI
10.1007/978-3-030-57097-2_5
Publisher site
See Chapter on Publisher Site

Abstract

[Applying tMCMC resolves two major problems we post at the beginning. One is the local minima problem and the other is a model selection problem. However, the observation tells us an inadequate performance when sampling a complex model. The decreased sampling efficiency is due to the dimensional changes. Hence, one possible solution comes to make MCMC methods more scalable and to be deployed on a high-performance computing system. The idea brings people to look for a parallel version of MCMC algorithms. In this chapter, we will introduce such a parallel schematic, which can be combined with any type of MCMC sampling method. We will first introduce the fundamental concept of tempering. And then we use the combination of tMCMC with parallel tempering to form up a complete workflow for solving a fast statistical inverse problem.]

Published: Aug 28, 2020

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