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Geometry of maximum likelihood estimation in Gaussian graphical models
Geometry of maximum likelihood estimation Gaussian graphical models
2011/1/21
We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. An algebraic elimination criterion allows us to nd exact lower bounds on the number of observations...
Non-asymptotic deviation inequalities for smoothed additive functionals in non-linear state-space models with applications to parameter estimation
Non-asymptotic deviation inequalities smoothed additive functionals in non-linear state-space parameter estimation
2011/2/22
Approximating joint smoothing distributions using particle-based methods is a well-known issue in statistical inference when operating on general state space hidden Markov models (HMM). In this paper ...
High-dimensional covariance estimation based on Gaussian graphical models
High-dimensional covariance estimation Gaussian graphical models
2010/12/15
Undirected graphs are often used to describe high dimensional distributions. Under sparsity
conditions, the graph can be estimated using ℓ1-penalization methods. We propose and study
the follo...