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Published in 2018 at "Journal of Fusion Energy"
DOI: 10.1007/s10894-018-0173-2
Abstract: The article presents a new approach to plasma neutron emissivity reconstruction based on the Metropolis–Hastings Monte Carlo algorithm. The algorithm is based on a biased random walk. A dedicated computer code generates pseudo-random samples within the…
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Keywords:
plasma;
monte carlo;
metropolis hastings;
neutron emissivity ... See more keywords
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Published in 2020 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2020.2983150
Abstract: The dimension and the complexity of inference problems have dramatically increased in statistical signal processing. It thus becomes mandatory to design improved proposal schemes in Metropolis-Hastings algorithms, providing large proposal transitions that are accepted with…
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Keywords:
metropolis hastings;
hastings algorithm;
majorize minimize;
minimize adapted ... See more keywords
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Published in 2022 at "Bernoulli"
DOI: 10.3150/21-bej1369
Abstract: Component-wise MCMC algorithms, including Gibbs and conditional Metropolis-Hastings samplers, are commonly used for sampling from multivariate probability distributions. A long-standing question regarding Gibbs algorithms is whether a deterministic-scan (systematic-scan) sampler converges faster than its random-scan…
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Keywords:
metropolis hastings;
conditional metropolis;
convergence rates;
two component ... See more keywords
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Published in 2021 at "Algorithms"
DOI: 10.3390/a14120351
Abstract: Markov chain Monte Carlo (MCMC) techniques are usually used to infer model parameters when closed-form inference is not feasible, with one of the simplest MCMC methods being the random walk Metropolis–Hastings (MH) algorithm. The MH…
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Keywords:
stochastic volatility;
volatility metropolis;
metropolis hastings;
effective sample ... See more keywords