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credible sets. When approximating such contours using Monte Carlo integration methods like importance sampling or Markov chain … numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate …
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Based on a record sample from the Rayleigh model, we consider the problem of estimating the scale and location parameters of the model and predicting the future unobserved record data. Maximum likelihood and Bayesian approaches under different loss functions are used to estimate the model's...
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Bayesian inference relies heavily on numerical Markov chain Monte carlo (MCMC) methods for the estimation of the typically intractable high-dimensional posterior distributions and requires specific inputs. In this paper we introduce a new general and efficient numerical approach to address...
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