Sentence examples for fitting framework for from inspiring English sources
The notion of visual prediction and competition between neural representations at various levels (i.e., between a strong bottom-up offset signal and a central overshooting representation), offers a fitting framework for conceptualizing the influence of motor resonance on the visual system.
After deriving a general fitting framework, we describe the constraints used in our specific case.
As all of our work aimed to promote health equity among marginalized populations, this was a fitting framework.
The major contributions of the neuro-tree classifier are the provisions of a new facility for the prevention of over fitting and the use of new fitness evaluation framework for maximizing the sensitivity and specificity.
Parameter estimation was carried out using the recently developed conditional inference framework for fitting tree-based models based on permutation tests.
Dynamic causal modelling is a framework for fitting differential equation (state space) models of neural states to neuroimaging data using Bayesian inference (Friston et al., 2003).
This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement.
Following the proposed assessment framework, selection of pipe size and types of fittings is considered in this framework for improvement of inherent SHE and EP, as those selection criteria are the key design parameters at this design stage.
Our aim is to embed the problem in a general framework for optimal Bayesian incremental fitting [14, 15], where a ranking of parameter values is used to incrementally train a user preference model.
In Subsection 3.1, we first derive a general framework for the inclusion of AAM fitting constraints.
We present here a generic framework for combined sensitivity analysis, parameter identifiability analysis, parameter fitting and model reduction, which can be applied to all types of deterministic models generating a set of outputs from a set of input parameters.
