Sensitivity estimations for Bayesian inference models solved by MCMC methods

Pérez, C.J., Martín, J. and M.J. Rufo

Reliability Engineering and System Safety, 2006, vol. 91, issue 10, 1310-1314

Abstract: The advent of Markov Chain Monte Carlo (MCMC) methods to simulate posterior distributions has virtually revolutionized the practice of Bayesian statistics. Unfortunately, sensitivity analysis in MCMC methods is a difficult task. In this paper, a computationally low-cost method to estimate local parametric sensitivities in Bayesian models is proposed. The sensitivity measure considered here is the gradient vector of a posterior quantity with respect to the parameter. The gradient vector components are estimated by using a result based on the integral/derivative interchange. The MCMC simulations used to estimate the posterior quantity can be re-used to estimate the sensitivity measures and their errors, avoiding the need for further sampling. The proposed method is easy to apply in practice as it is shown with an illustrative example.

Keywords: Parametric sensitivity; MCMC; Bayesian inference; Bayesian decision theory (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:91:y:2006:i:10:p:1310-1314

DOI: 10.1016/j.ress.2005.11.029

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