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Applications of Gaussian-Inverse Wishart Process Regression Models in Claims Reserving
VarianceGaussian processes are stochastic processes based on the normal distribution (i.e., collections of normal random variables indexed by a mathematical set). In the context of probability theory and statistics, these processes are well-known and well-behaved objects that have been extensively explored and used.
Marco De Virgilis, Giulio Carnevale
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When is the Use of Gaussian-inverse Wishart-Haar Priors Appropriate?
Federal Reserve Bank of Dallas, Working PapersAtsushi Inoue, Lutz Kilian
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Mixtures of traces of Wishart and inverse Wishart matrices
Communications in Statistics - Theory and Methods, 2021Jolanta Pielaszkiewicz
exaly
Corrigendum: Applications of Gaussian-Inverse Wishart Process Regression Models in Claims Reserving
VarianceMarco De Virgilis, Giulio Carnevale
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Efficient Gaussian graphical model determination under G-Wishart prior distributions
Electronic Journal of Statistics, 2012Sophia Zhengzi Li
exaly
On the mean and variance of the generalized inverse of a singular Wishart matrix
Electronic Journal of Statistics, 2011R Dennis Cook, Liliana Forzani
exaly
On the reduction of Gaussian inverse Wishart mixtures.
Karl Granström, Umut Orguneropenaire +1 more source
Random continued fractions and inverse Gaussian distribution on a symmetric cone
Journal of Theoretical Probability, 1995exaly