To this aim, we trained and tested a Classification and Regression Tree (CART) model, using the 16 kinematic parameters of interested as predictors (see ''Motion capture and video recording'' section) and the intention choice by participants (to pour vs. to drink; 7114 responses; 1.2% missed responses) as a categorical outcome.
In early SFL, some evidence of discussion about the scope of the parameters among interested scholars can certainly be found, but solutions usually took the form of suggesting a greater number of parameters (for example, Fawcett, [1980]; Gregory and Carroll, [1978]; Ure and Ellis, [1977]; Hasan, [1973]).
While sampling 25 loci will increase the statistical confidence in population genetic parameters, researchers interested in comparing parameter estimates between taxa should remain cautious in attributing biological significance to statistical differences [39].
Totally 100,000 samples for each parameter of interested were generated, with a burn-in of 20,000 iterations to avoid the influence of the initial values.
These parameters help users interested in applications such as satellite radar retrievals of rainfall rate, GPM satellite radar validation, or high-resolution model evaluation.
In this case we explicitly list the parameters we are interested in (g2.ellip and g2.theta) to avoid calculating the limits for the other parameters.
The stochastic simulations have quantified the uncertainty of random viscosity model parameters on the interested flow parameters wall shear stress and pressure for two Reynolds numbers: Re=212 and Re=21.
({mathbf{Y}}_{{omega_{j} }}) is the vector of the input Laplace seismic spectrum, and ({mathbf{m}}) is the vector of interested parameters.
To mitigate the model-dependent problem of seismic inversion, geophysicists have attempted to deploy inversion without a precise preliminary model and exploit the limited data to derive the ultra-low-frequency information of our interested parameters.
In selecting values for these parameters, we are interested in finding the set which provide a good segmentation accuracy without adding significant computational cost (as would be the case with more trees, more training data, etc).
