On the multivariate Laplace distribution

By Torbjørn Eltoft, Taesu Kim, Te-Won Lee. IEEE Signal Processing Letters, 2006

318 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Linear quantile mixed models (2013, result): “Even though it is easy to re-scale Ψ to a diagonal matrix, the joint density does not factorize into q AL variates (Eltoft et al. 2006) and, therefore, the q-dimensional quadrature can not be based on q successive applications of one-dimensional rules. Therefore, at the moment, we do not consider an”
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation (2014, background): “Note that unlike the multivariate Gaussian, it is not uniquely de-ﬁned [5].”
On posterior contraction of parameters and interpretability in Bayesian mixture modeling (2019, unknown): “As discussed in [8], Km(x) ∼ √ π 2x exp(−x) as |x| → ∞.”
Associating neural word embeddings with deep image representations using Fisher Vectors (2015, background): “Note that unlike the multivariate Gaussian, it is not uniquely defined [6].”
Variational Inference for Sparse and Undirected Models (2016, methodology): “In the real protein experiment, we also consider an exponential hyperprior, which corresponds to a Multivariate Laplace distribution (Eltoft et al., 2006) over the groups.”
Modeling asymptotically independent spatial extremes based on Laplace random fields (2015, background): “Multivariate Laplace distributions are also discussed in Eltoft et al. (2006).”