Bayesian estimation of probabilistic sensitivity measures

Computer experiments are becoming increasingly important in scientific investigations. In the presence of uncertainty, analysts employ probabilistic sensitivity methods to identify the key-drivers of change in the quantities of interest. Simulation complexity, large dimensionality and long running times may force analysts to make statistical inference at small sample sizes. Methods designed to estimate probabilistic sensitivity measures at relatively low computational costs are attracting increasing interest. We propose a fully Bayesian approach to the estimation of probabilistic sensitivity measures based on a one-sample design. We discuss, first, new estimators based on placing piecewise constant priors on the conditional distributions of the output given each input, by partitioning the input space. We then present two alternatives, based on Bayesian non-parametric density estimation, which bypass the need for predefined partitions. In all cases, the Bayesian paradigm guarantees the quantification of uncertainty in the estimation process through the posterior distribution over the sensitivity measures, without requiring additional simulator evaluations. The performance of the proposed methods is compared to that of traditional point estimators in a series of numerical experiments comprising synthetic but challenging simulators, as well as a realistic application. A Revised Version of the Manuscript is under Review at Statistics and Computing.

READ FULL TEXT

page 2

page 21

research
10/08/2018

Geometric Sensitivity Measures for Bayesian Nonparametric Density Estimation Models

We propose a geometric framework to assess global sensitivity in Bayesia...
research
01/29/2018

Target and Conditional Sensitivity Analysis with Emphasis on Dependence Measures

In the context of sensitivity analysis of complex phenomena in presence ...
research
09/06/2019

A review of Approximate Bayesian Computation methods via density estimation: inference for simulator-models

This paper provides a review of Approximate Bayesian Computation (ABC) m...
research
07/12/2022

Neural Posterior Estimation with Differentiable Simulators

Simulation-Based Inference (SBI) is a promising Bayesian inference frame...
research
06/13/2022

Density Estimation with Autoregressive Bayesian Predictives

Bayesian methods are a popular choice for statistical inference in small...
research
08/29/2019

Vectorized Uncertainty Propagation and Input Probability Sensitivity Analysis

In this article we construct a theoretical and computational process for...
research
08/15/2021

NPBDREG: A Non-parametric Bayesian Deep-Learning Based Approach for Diffeomorphic Brain MRI Registration

Quantification of uncertainty in deep-neural-networks (DNN) based image ...

Please sign up or login with your details

Forgot password? Click here to reset