Numerical Bayesian inference with arbitrary prior |
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Authors: | Efthymios G Tsionas |
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Institution: | (1) Council of Economic Advisers Ministry of National Economy, 5 Nikis Street Constitution Square, 10180 Athens, Greece |
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Abstract: | The purpose of the paper, is to explain how recent advances in Markov Chain Monte Carlo integration can facilitate the routine
Bayesian analysis of the linear model when the prior distribution is completely user dependent. The method is based on a Metropolis-Hastings
algorithm with a Student-t source distribution that can generate posterior moments as well as marginal posterior densities
for model parameters. The method is illustrated with numerical examples where the combination of prior and likelihood information
leads to multimodal posteriors due to prior-likelihood conflicts, and to cases where prior information can be summarized by
symmetric stable Paretian distributions. |
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Keywords: | Bayesian inference linear model prior information stable distributions |
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