Sentence examples for likelihood fit from inspiring English sources

Two alternative algorithms for the calculation of the maximum likelihood fit are considered (gradient-based minimization and differential evolution).

In contrast, in a sensitivity analysis, we show that the maximum likelihood fit (mle) is not robust.

A maximum likelihood fit to Figure 2 of Harushima et al. (1998) shows that an Erlang distribution with m=2,λ=1/2 fits rice data well.

The outputs of two neural networks designed to identify b→ℓ− and c→ℓ+ decays are used in a maximum likelihood fit to a sample of events containing one or two identified leptons.

To verify formally whether or not the distributions do indeed exhibit power law behavior we performed a maximum likelihood fit for a power law distribution using the methodology developed by Clauset et al. for analyzing power law behavior in empirical data [41].

In this task, the boundary of what is reachable was determined using a maximum likelihood fit procedure based on the second-order derivatives (quasi-newton method) to obtain the logit regression model that best fitted the (yes-no) responses of the subject for the seven distances of the target according to arm length (0cm, ±3cm, ±6cm, ±12cm).

Analyses were carried out by average information restricted maximum likelihood, fitting four single-trait repeatability animal models with various combinations of individual and maternal effects.

Given the basis patterns, we invert an observed image to deduce the basis pattern coefficients by using maximum likelihood fitting for Poisson distributed uncertainties.

Given the basis patterns, we invert an observed image to deduce their coefficients by using maximum likelihood fitting for Poisson distributed uncertainties.

After densely sampling φp we choose the maximum of all the maximum likelihood fits to select the best φp and coefficients of <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0016772.e077.PNG" class= inline-graphic"/> then compute from them the background light intensity level, Fb, signal intensity, F0, and angle θp.

Following Pinheiro and Bates (2000), maximum likelihood fitted models were used when models were compared, whereas a restricted maximum likelihood fitted model was used for parameter estimation.