The convergence rate of the TM algorithm of Edwards & Lauritzen
BiometrikaPublished 1 June 2002
Rolf Sundberg
Citations25
SJR quartileQ1
SJR score3.60
SNIP2.67
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A simple expression, in terms of observed information matrices, is given for the convergence rate of the algorithm per se, when it converges, and the result elucidates also in which situations the algorithm will require a line search.
Abstract
Journal Article The convergence rate of the TM algorithm of Edwards & Lauritzen Get access Rolf Sundberg Rolf Sundberg Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 89, Issue 2, June 2002, Pages 478–483, https://doi.org/10.1093/biomet/89.2.478 Published: 01 June 2002
Keywords
Computer ScienceMathematics
Journal of the Royal Statistical Society Series B (Statistical Methodology)Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
49,657 Citations1977A. P. Dempster, N. M. Laird +1 more
Communications in Statistics - Simulation and ComputationAn iterative method for solution of the likelihood equations for incomplete data from exponential families
109 Citations1976Rolf Sundberg
BiometrikaThe TM algorithm for maximising a conditional likelihood function
50 Citations2001David Edwards, Steffen L. Lauritzen
An algorithm for maximising a conditional likelihood function when the corresponding unconditional likelihood function is more easily maximised, which applies to mixed graphical chain models and was developed with these in mind, but it may have applications beyond these.