Testing that a Local Optimum of the Likelihood is Globally Optimum Using Reparameterized Embeddings-Reference-Cited by-同舟云学术

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Testing that a Local Optimum of the Likelihood is Globally Optimum Using Reparameterized Embeddings

Published:2020-07 Issue:6-7 Volume:62 Page:858-871
ISSN:0924-9907
Container-title:Journal of Mathematical Imaging and Vision
language:en
Short-container-title:J Math Imaging Vis

Author:

LeBlanc Joel W.^ORCID,Thelen Brian J.,Hero Alfred O.

Funder

Army Research Office

U.S. Department of Energy

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Geometry and Topology,Computer Vision and Pattern Recognition,Condensed Matter Physics,Modeling and Simulation,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s10851-020-00979-0.pdf

Reference45 articles.

1. Alberge, F., Nikolova, M., Duhamel, P.: Blind identification/equalization using deterministic maximum likelihood and a partial prior on the input. IEEE Trans. Signal Process. 54(2), 724–737 (2006)

2. Andrieu, C., Doucet, A.: Simulated annealing for maximum a posteriori parameter estimation of hidden Markov models. IEEE Trans. Inform. Theory 46(3), 994–1004 (2000)

3. Andrieu, C., Doucet, A., Fitzgerald, W.J.: An introduction to Monte Carlo methods for Bayesian data analysis. In: Mees, A.I. (ed.) Nonlinear Dynamics and Statistics, pp. 169–217. Springer, Berlin (2001)

4. Barakat, R., Sandler, B.H.: Determination of the wave-front aberration function from measured values of the point-spread function: a two-dimensional phase retrieval problem. JOSA A 9(10), 1715–1723 (1992)

5. Biernacki, C.: Testing for a global maximum of the likelihood. J. Comput. Graph. Stat. 14(3), 657–674 (2005)

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