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While adding these features improves network performance, it adds more complexity to the models estimation and the application performance, especially when applying it to large size vocabulary applications or language with rich morphological features.
The lower of the two levels consists of adaptive system-identification algorithms borrowed from signal processing, especially, Hidden Markov Model (HMM) methods [Hidden Markov Models: Estimation and Control (1995) Springer Verlag].
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Three patterns of estimates were made: estimates made from logarithmic approximations of all items (logarithm-model estimation), estimates made from linear approximations of all items (linear-model estimation), and estimates made from approximations of items with higher determination coefficients (mixed-model estimation).
Model Estimation and Selection: We estimated the posterior distributions of the hierarchical Bayesian models using Markov chain Monte Carlo (MCMC) methods.
Section 4.2 presents the proposed adaptive motion smoothing using multiple-model estimation and how to modify it with estimate projection.
Moreover, model estimation and fitting vary as well.
Residuals are the difference between expected scores, based on model estimation, and actual results.
Functions for model estimation and image transformation are provided by the image processing toolbox in MATLAB.
Model estimation and data simulation were performed using NONMEM version 5.1.
Worker and firm controls are included in each model estimation and presented in Table 8 in the Appendix.
The main idea is to provide a common formatting system for data generation, extraction, curation, model estimation and validation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com