Estimating Ocean Circulation: An MCMC Approach With Approximated Likelihoods via the Bernoulli Factory
| Autor: | Radu Herbei, L. Mark Berliner |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Mathematical optimization Partial differential equation Bayesian probability Markov chain Monte Carlo Regular grid Bernoulli's principle symbols.namesake symbols Factory (object-oriented programming) Data pre-processing Statistics Probability and Uncertainty Likelihood function Mathematics |
| Zdroj: | Journal of the American Statistical Association. 109:944-954 |
| ISSN: | 1537-274X 0162-1459 |
| DOI: | 10.1080/01621459.2014.914439 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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