Hain, "Error approximation and minimum phone error acoustic model estimation," IEEE Audio, Speech, Lang. Process., vol. 18, no. 6, pp. 1269-1279, Aug. 2010.M. Gibson and T. Hain "Error approximation and min
In this analysis, L = 200 has been considered, as a good trade-off between the obtained standard deviation of the channel estimation error and the typical overhead in RL traffic. The measured MMSE/MMSE-SIC packet error rate performance loss is ¡ 0.5 dB. In the next section, a ...
overlaps with true cell type-specific features, goodness of fit, and root mean squared error (RMSE) of the fitted deconvolution model. These metrics quantify the deconvolution results from the quality of
In the present work, we introduce a VEMcomputableerror estimator for a two dimensional model problem and we prove lower and upper bounds with respect to the energy error that are explicit both in the mesh size and in the distribution of local degrees of accuracy (which are the VEM counterpart...
Error Estimation of Hybrid Rational Function ApproximationHybrid Raional Function Approximation (HRFA) is one of the most important applications of approximate-GCD algorithms and is applied to several practical problems such as data smoothing, integral and others. Classical rational interpolation may not ...
tion Error Approach Accounting for Modelling Errors in Parameter Estimation Problems: The Bayesian Approximation Error ApproachAccounting for Modelling Errors in Parameter Estimation Problems: The Bayesian Approximation Error ApproachMany parameter estimation problems are highly sensitive to errors. The Bayesian ...
3. Brief review of energy norm error estimators The approximation of the continuous displacements and test functions in the virtual work equation (2.2) by the finite element shape functions leads to discretization errors. The energy norm is a natural choice to measure the errors e +u *u h due...
An up-to-date, one-stop reference-complete with applications This volume presents the most up-to-date information available on a posteriori error estimation for finite element approximation in mechanics and mathematics. It emphasizes methods for elliptic boundary value problems and includes applications ...
Fig. 2: Multi-animal DeepLabCut keypoint detection and whole-body assembly performance. a, Distribution of keypoint prediction error for DLCRNet_ms5 with stride 8 (70% train and 30% test split). Violin plots display train (top) and test (bottom) errors. Vertical dotted lines are the firs...
When increasing the trajectory lengths we see lower error predictions for all models. Both FBM and SBM achieve lower predicted errors than the other three models, despite the larger range ofα, which may be attributed to the fact that they do not rely on hidden waiting times, in contrast to...