Results 21 to 30 of about 807,423 (305)
Scaling Vision with Sparse Mixture of Experts
CoRR, 2021 44 pages, 38 ...
Carlos Riquelme +7 more
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A Bayesian Predictive Discriminant Analysis with Screened Data
Entropy, 2015 In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme.
Hea-Jung Kim
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Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models. [PDF]
, 2010 The label switching problem is caused by the likelihood of a Bayesian mixture model being invariant to permutations of the labels. The permutation can change multiple times between Markov Chain Monte Carlo (MCMC) iterations making it difficult to infer ...
Sperrin, M.; id_orcid +9 more
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Scale Mixtures of Normal Distributions
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1974 Summary This paper presents necessary and sufficient conditions under which a random variable X may be generated as the ratio Z/V where Z and V are independent and Z has a standard normal distribution. This representation is useful in Monte Carlo calculations.
Andrews, D. F., Mallows, C. L.
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Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior
IET Signal Processing, 2022 Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed.
Qilei Zhang, Lei Yu, Feng He, Yifei Ji
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Estimating Tail Probabilities of Random Sums of Phase-Type Scale Mixture Random Variables
Algorithms, 2022 We consider the problem of estimating tail probabilities of random sums of scale mixture of phase-type distributions—a class of distributions corresponding to random variables which can be represented as a product of a non-negative but otherwise ...
Hui Yao, Thomas Taimre
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Online Quantum Mixture Regression for Trajectory Learning by Demonstration [PDF]
, 2013 16/01/14 MEB. Pre-print version OK to add.In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states,
Dimitrios Korkinof +3 more
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The Q-Matrix Anchored Mixture Rasch Model
Frontiers in Psychology, 2021 Mixture item response theory (IRT) models include a mixture of latent subpopulations such that there are qualitative differences between subgroups but within each subpopulation the measure model based on a continuous latent variable holds.
Ming-Chi Tseng, Wen-Chung Wang
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Exact tail asymptotics in bivariate scale mixture models [PDF]
, 2011 Let (X, Y) = (RU (1), RU (2)) be a given bivariate scale mixture random vector, with R > 0 independent of the bivariate random vector (U (1), U (2)). In this paper we derive exact asymptotic expansions of the joint survivor probability of (X, Y) assuming
Hashorva, E. +2 more
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The impact of ordinal scales on Gaussian mixture recovery
Behavior Research Methods, 2022 AbstractGaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation–maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC).
Jonas M B Haslbeck +2 more
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