Statistical and computational tradeoffs in stochastic composite likelihood

Journal of Machine Learning Research

Volumn 5, Issue , 2009, Pages 129-136

  Dillon, Joshua V ^a   Lebanon, Guy ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC VARIANCE; BOLTZMANN MACHINES; COMPOSITE LIKELIHOOD; CONDITIONAL RANDOM FIELD; EXPERIMENTAL STUDIES; MAXIMUM LIKELIHOOD ESTIMATOR; STATISTICAL ACCURACY; STOCHASTIC VARIATION;

ARTIFICIAL INTELLIGENCE; ASYMPTOTIC ANALYSIS; COMPUTATIONAL COMPLEXITY; STOCHASTIC SYSTEMS;

ESTIMATION;

EID: 84862304027     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (13)

1. B. Arnold and D. Strauss. Pseudolikelihood estimation: some examples. Sankhya B, 53:233-243, 1991.
2. J. Besag. Spatial interaction and the statistical analysis of lattice systems (with discussion). J Roy Statist Soc B, 36(2):192-236, 1974.
3. R. Casella and C. Robert. Monte Carlo Statistical Methods. Springer Verlag, second edition, 2004.
4. T. S. Ferguson. A Course in Large Sample Theory. Chapman & Hall, 1996.
5. N. Hjort and C. Varin. ML, PL, and QL in markov chain models. Scand J Stat, 35(1):64-82, 2008.
6. G. Liang and B. Yu. Maximum pseudo likelihood estimation in network tomography. IEEE T Signal Proces, 51 (8):2043-2053, 2003.
7. P. Liang and M. I. Jordan. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. In Proc. of the International Conference on Machine Learning, 2008.
8. B. G. Lindsay. Composite likelihood methods. Contemporary Mathematics, 80:221-239, 1988.
9. Y. Mao and G. Lebanon. Isotonic conditional random fields and local sentiment flow. In Advances in Neural Information Processing Systems 19, pages 961-968, 2007.
10. C. Sutton and A. McCallum. Piecewise pseudolikelihood for efficient training of conditional random fields. In Proc. of the International Conference on Machine Learning, 2007.
11. C. Varin and P. Vidoni. A note on composite likelihood inference and model selection. Biometrika, 92:519-528, 2005.
12. E. P. Xing, M. I. Jordan, and S. Russell. A generalized mean field algorithm for variational inference in exponential families. In Uncertainty in Artificial Intelligence, 2003.
13. S.-C. Zhu and X. Liu. Learning in Gibbsian fields: How accurate and how fast can it be? IEEE T Pattern Anal, 24(7):1001-1006, 2002.