Estimating ocean circulation: : an MCMC approach with approximated likelihoods via the bernoulli factory

• Autores: Radu Herbei, L. Mark Berliner
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 109, Nº 507, 2014, págs. 944-954
• Idioma: inglés
• DOI: 10.1080/01621459.2014.914439
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• Resumen

  • We provide a Bayesian analysis of ocean circulation based on data collected in the South Atlantic Ocean. The analysis incorporates a reaction-diffusion partial differential equation that is not solvable in closed form. This leads to an intractable likelihood function. We describe a novel Markov chain Monte Carlo approach that does not require a likelihood evaluation. Rather, we use unbiased estimates of the likelihood and a Bernoulli factory to decide whether or not proposed states are accepted. The variates required to estimate the likelihood function are obtained via a Feynman�Kac representation. This lifts the common restriction of selecting a regular grid for the physical model and eliminates the need for data preprocessing. We implement our approach using the parallel graphic processing unit (GPU) computing environment.