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Continuous Herded Gibbs Sampling
2021 IEEE 24th International Conference on Information Fusion (FUSION), 2021 Herding is a technique to sequentially generate deterministic samples from a probability distribution. In this work, we propose a continuous herded Gibbs sampler that combines kernel herding on continuous densities with the Gibbs sampling idea. Our algorithm allows for deterministically sampling from high-dimensional multivariate probability densities,
Wolf, Laura M., Baum, Marcus
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Jurnal Matematika Integratif, 2022 Parameter dari suatu distribusi biasanya belum diketahui nilainya, untuk mengetahuinya dilakukan estimasi terhadap parameter tersebut. Metode estimasi parameter ada dua macam, yaitu metode klasik dan metode Bayesian.
Rahmayanti Putri Desiresta +2 more
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Near-Optimal Detection in MIMO Systems using Gibbs Sampling [PDF]
, 2009 In this paper we study a Markov Chain Monte Carlo (MCMC) Gibbs sampler for solving the integer least-squares problem. In digital communication the problem is equivalent to performing Maximum Likelihood (ML) detection in Multiple-Input Multiple-Output ...
Dimakis, Alexandros G. +3 more
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Gibbs Sampling gives Quantum Advantage at Constant Temperatures with O(1)-Local Hamiltonians [PDF]
QuantumSampling from Gibbs states – states corresponding to system in thermal equilibrium – has recently been shown to be a task for which quantum computers are expected to achieve super-polynomial speed-up compared to classical computers, provided the locality
Joel Rajakumar, James D. Watson
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Improved techniques for sampling complex pedigrees with the Gibbs sampler
Genetics Selection Evolution, 2007 Markov chain Monte Carlo (MCMC) methods have been widely used to overcome computational problems in linkage and segregation analyses. Many variants of this approach exist and are practiced; among the most popular is the Gibbs sampler.
Fernando Rohan L +2 more
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Empirical Bayes Gibbs sampling [PDF]
Biostatistics, 2001 The wide applicability of Gibbs sampling has increased the use of more complex and multi-level hierarchical models. To use these models entails dealing with hyperparameters in the deeper levels of a hierarchy. There are three typical methods for dealing with these hyperparameters: specify them, estimate them, or use a 'flat' prior.
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Statistics and ComputingIn some applied scenarios, the availability of complete data is restricted, often due to privacy concerns; only aggregated, robust and inefficient statistics derived from the data are made accessible. These robust statistics are not sufficient, but they demonstrate reduced sensitivity to outliers and offer enhanced data protection due to their higher ...
Luciano, A, Robert, CP, Ryder, RJ
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Coalition Formation Game for Cooperative Cognitive Radio Using Gibbs Sampling [PDF]
, 2014 This paper considers a cognitive radio network in which each secondary user selects a primary user to assist in order to get a chance of accessing the primary user channel.
Abuzainab, Nof +2 more
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On Bayesian Estimation in Mixed Linear Models Using the Gibbs Sampler [PDF]
المجلة العراقية للعلوم الاحصائية, 2008 This paper tackles the estimation of parameters of linear mixed random effect one–classification model by Bayesian technique which includes Gibbs sampling. Gibbs sampling is a special case of Monte Carlo Method which uses Markov Chain and so called MCMC (
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Evolution with recombination as Gibbs sampling
Theoretical Population Biology, 2023 This work presents a population genetic model of evolution, which includes haploid selection, mutation, recombination, and drift. The mutation-selection equilibrium can be expressed exactly in closed form for arbitrary fitness functions without resorting to diffusion approximations.
J.M. Poulton (Jenny) +2 more
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