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Abstract: This article describes an algorithm for maximising a conditional likelihood function when the corresponding unconditional likelihood function is more easily maximised. The algorithm is similar to the EM algorithm but di erent as the parameters rather than the data are augmented and the conditional rather than the marginal likelihood function is maximised. In exponential families the algorithm takes a particular simple form which is computationally very close to the EM algorithm. The algorithm... (Update)
Context of citations to this paper: More
.... have to resort to gradient descent [20] 105] Other variants of gradient descent and line search are also emerging in statistics [50]. Recently, the conditional version of the EM algorithm has been proposed in the CEM algorithm [92] 94] and will be discussed further...
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BibTeX entry: (Update)
D. Edwards and S. Lauritzen. The tm algorithm for maximising a conditional likelihood function. To Appear in Biometrika, 2001. http://citeseer.ist.psu.edu/edwards01tm.html More
@misc{ edwards01tm,
author = "D. Edwards and S. Lauritzen",
title = "The tm algorithm for maximising a conditional likelihood function",
text = "D. Edwards and S. Lauritzen. The tm algorithm for maximising a conditional
likelihood function. To Appear in Biometrika, 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/edwards01tm.html" }
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1 On stochastic versions of the em algorithm (context) - Patil, Kotz et al. - 2001
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