Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation

Computational Statistics and Data Analysis

Volumn 91, Issue , 2015, Pages 4-19

  Allassonnière, Stéphanie ^a   Kuhn, Estelle ^b  

^a CENTRE DE MATHÉMATIQUES APPLIQUÉES   (France)

^b UR341 National Agronomical Research Institute (INRA)   (France)

Author keywords

Anisotropic MALA; Deformable template; Geometric variability; High dimension; Maximum likelihood estimation; MCMC; Missing variable; Stochastic EM algorithm

Indexed keywords

ANISOTROPY; CHAINS; CLUSTERING ALGORITHMS; DEFORMATION; IMAGE SEGMENTATION; MARKOV PROCESSES; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; MAXIMUM PRINCIPLE; MEDICAL IMAGING; MONTE CARLO METHODS; PARAMETER ESTIMATION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

ANISOTROPIC MALA; DEFORMABLE TEMPLATES; GEOMETRIC VARIABILITY; HIGH DIMENSIONS; MCMC; MISSING VARIABLE; STOCHASTIC EM;

ALGORITHMS;

EID: 84936764514     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2015.04.011     Document Type: Article

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