Approximate Bayesian Inference for Piecewise-Linear Stiffness Systems

Nayek, Rajdip; Abdessalem, Mohamed Anis Ben; Dervilis, Nikolaos; Cross, Elizabeth; Worden, Keith

doi:10.1007/978-3-031-04086-3_23

Cited by 1 publication

(1 citation statement)

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“…Approximate Bayesian Computation (ABC) is another alternative to MCMC. In recent likelihood-free procedures, ABC [44] is the most commonly used family of algorithms (e.g., [3,4,26,29,33,40]).…”

Section: Introductionmentioning

confidence: 99%

Conditional density estimation using population Monte Carlo based approximate Bayesian computation

AFZAAL

Ilyas

2023

Hacettepe Journal of Mathematics and Statistics

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Most statistical methods require likelihood evaluation to draw a statistical inference. However, in some situations, likelihood evaluation becomes difficult analytically or computationally. Different likelihood-free methods are available that eliminate the need to compute the likelihood function. Approximate Bayesian Computation (ABC) is a framework that implements likelihood-free inference and replaces the likelihood evaluation with simulations by using forward modeling. The goal of ABC methods is to approximate the posterior distribution. However, posterior approximation via ABC methods is still considerably expensive for high dimensions. ABC requires many simulations that become computationally infeasible for complex models. Here, a technique is proposed that combines a somewhat more efficient form of ABC (Population Monte Carlo, PMC) with a Conditional Density Estimation (CDE) approach. The proposed framework provides an estimation of the posterior distribution which is referred to as PMC-CDE. A simulation study is performed that provides empirical evidence to show the efficiency of PMC-CDE in terms of integrated squared error loss. Furthermore, real-life datasets manifest the application of the proposed method.

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