Wishart distributions: Advances in theory with Bayesian application
نویسندگان
چکیده
منابع مشابه
Hartigan’s method for k-MLE : Mixture modeling with Wishart distributions and its application to motion retrieval
We describe a novel algorithm called k-Maximum Likelihood Estimator (k-MLE) for learning finite statistical mixtures of exponential families relying on Hartigan’s k-means swap clustering method. To illustrate this versatile Hartigan k-MLE technique, we consider the exponential family of Wishart distributions and show how to learn their mixtures. First, given a set of symmetric positive definite...
متن کاملThe Wishart Distributions on Homogeneous Cones
The classical family of Wishart distributions on a cone of positive definite matrices and its fundamental features are extended to a family of generalized Wishart distributions on a homogeneous cone using the theory of exponential families. The generalized Wishart distributions include all known families of Wishart distributions as special cases. The relations to graphical models and Bayesian s...
متن کاملOn Riesz and Wishart distributions associated with decomposable undirected graphs
Classical Wishart distributions on the open convex cones of positive definite matrices and their fundamental features are extended to generalized Riesz and Wishart distributions associated with decomposable undirected graphs using the basic theory of exponential families. The families of these distributions are parameterized by their expectations/natural parameter and multivariate shape paramet...
متن کاملWISHART DISTRIBUTIONS FOR COVARIANCE GRAPH MODELS By
Gaussian covariance graph models encode marginal independence among the components of a multivariate random vector by means of a graph G. These models are distinctly different from the traditional concentration graph models (often also referred to as Gaus-sian graphical models or covariance selection models), as the zeroes in the parameter are now reflected in the covariance matrix Σ, as compar...
متن کاملHamiltonian Monte Carlo sampling for Wishart distributions with eigenvalue constraints
Sampling from constrained target spaces for Bayesian inference is a non-trivial problem. A recent development has been the use of Hamiltonian Monte Carlo in combination with particle re ection, see [Pakman and Paninski, 2014]. However, Hamiltonian Monte Carlo is sensitive to several hyper parameters, that need to be tuned, to ensure an ecient sampler. For this purpose, [Wang et al., 2013] sugge...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2017
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2016.12.002