Optimal recovery of the synchronization parameters (timing, phase and frequency offsets) is analytically intractable and, as a consequence, most existing synchronization methods are either heuristic or based on approximate maximum likelihood (ML) arguments.
Parameter estimation by maximum likelihood is straightforward.
The parameter estimation method was maximum likelihood.
Parameter estimation is by maximum likelihood or by an extension of restricted maximum likelihood.
Parameter estimation was obtained by maximum likelihood.
The maximum likelihood method was used to fit the model to data in HP (i.e. argument of goodness-of-fit "logLik").
The authors state several arguments concerning the advantages of optimizing an error criterion instead of using a maximum likelihood approach for that, as employed by the work of Roth and Black [18].
If those combinatorial classes are to correspond to a considered SCFG, we have to face the problem that the maximum likelihood (ML) training introduces rational weights for the production rules while weighting as an admissible construction needs integer arguments.
full information maximum likelihood.
Weighted maximum likelihood estimation.
