Working Paper Article Version 1 This version is not peer-reviewed

Partial Inversion of the Elliptic Operator to Speed Up Computation of Likelihood in Bayesian Inference

Version 1 : Received: 4 August 2020 / Approved: 6 August 2020 / Online: 6 August 2020 (10:42:56 CEST)

How to cite: Litvinenko, A. Partial Inversion of the Elliptic Operator to Speed Up Computation of Likelihood in Bayesian Inference. Preprints 2020, 2020080158 Litvinenko, A. Partial Inversion of the Elliptic Operator to Speed Up Computation of Likelihood in Bayesian Inference. Preprints 2020, 2020080158

Abstract

Often, when solving forward, inverse or data assimilation problems, only a part of the solution is needed. As a model, we consider the stationary diffusion problem. We demonstrate an algorithm that can compute only a part or a functional of the solution, without calculating the full inversion operator and the complete solution. It is a well-known fact about partial differential equations that the solution at each discretisation point depends on the solutions at all other discretisation points. Therefore, it is impossible to compute the solution only at one point, without calculating the solution at all other points. The standard numerical methods like a conjugate gradient or Gauss elimination compute the whole solution and/or the complete inverse operator. We suggest a method which can compute the solution of the given partial differential equation 1) at a point; 2) at few points; 3) on an interface; or a functional of the solution, without computing the solution at all points. The required storage cost and computational resources will be lower as in the standard approach. With this new method, we can speed up, for instance, computation of the innovation in filtering or the likelihood distribution, which measures the data misfit (mismatch). Further, we can speed up the solution of the regression, Bayesian inversion, data assimilation, and Kalman filter update problems. Applying additionally the hierarchical matrix approximation, we reduce the cubic computational cost to almost linear $\mathcal{O}(k^2n \log^2 n)$, where $k\ll n$ and $n$ is the number of degrees of freedom. Up to the hierarchical matrix approximation error, the computed solution is exact. One of the disadvantages of this method is the need to modify the existing deterministic solver.

Subject Areas

innovation; data misfit; likelihood; Bayesian formula; partial inverse; domain decomposition; FEM; hierarchical matrices

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