Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo

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http://hdl.handle.net/10138/312419

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Hartmann , M & Ehlers , R S 2017 , ' Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo ' , Communications in Statistics: Simulation and Computation , vol. 46 , no. 7 , pp. 5285-5302 . https://doi.org/10.1080/03610918.2016.1152365

Title: Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo
Author: Hartmann, Marcelo; Ehlers, Ricardo S.
Contributor: University of Helsinki, Research Centre for Ecological Change
Date: 2017
Language: eng
Number of pages: 18
Belongs to series: Communications in Statistics: Simulation and Computation
ISSN: 0361-0918
URI: http://hdl.handle.net/10138/312419
Abstract: In this article, we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian approach using traditional Metropolis-Hastings methods, Hamiltonian Monte Carlo (HMC), and Riemann manifold HMC (RMHMC) methods to obtain the approximations to the posterior marginal distributions of interest. Applications to real datasets and simulation studies provide evidence that the extra analytical work involved in Hamiltonian Monte Carlo algorithms is compensated by a more efficient exploration of the parameter space.
Subject: 112 Statistics and probability
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