Sentences with phrase «large number of parameters»

It is weird for me, coming from molecular modeling (and a general interest in the history of science), to see people paying so much attention to matching a very small number of observations given the large number of parameters in the model.

This is a very important panel that measures a large number of parameters, giving a good overview of the health and function of many body organs.

Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used.

The relatively reliable climate state, and wide range of CS in the HadSM3 - AS may also, therefore, be as a result of a larger number of parameters having been varied in these ensembles, or other factors relating to the design of the ensembles.

Not exact matches

DCNNs typically use a large number of intermediate images and trainable parameters, often more than 100 million, to achieve results for difficult problems.

The researchers found that «1) ingestion of a large number of calories at one time (binge eating) impacts metabolic parameters even when total calories and macronutrients are appropriate for weight; 2) the timing of energy intake is an independent determinant of the diurnal rhythm of leptin secretion, indicating a relatively acute affect of energy balance on leptin dynamics; 3) the mechanism of exaggerated insulin secretion after a binge meal remains to be determined, but may be related to the altered diurnal pattern of leptin secretion; and 4) as most binge eating episodes in the population are associated with the ingestion of excess calories, it is hypothesized that binge eating behavior is associated with even greater metabolic dysfunction than that described herein.»

The varied a large number of things around reasonable ranges — the emissions, sensitivity, carbon cycle parameters, while optimising against observed data.

To understand the latter, and the parameters that influence them, we need to resort to models that themselves are subject to a large number of variables, not all of which we can properly calculate for any given point in time.

They can't even predict the next decade, much less ten decades; despite tuning they only poorly replicate the historical climate; their equations can't be shown to converge; the number of tunable parameters is far too large for comfort; they show absolutely no skill at regional scales; their results for things they are not tuned to replicate (e.g. rainfall) are abysmal — in short they are glorified Tinkertoy ™ models which have one common characteristic... they don't work well.

For any assumed distribution of parameter values, a method of producing 5 — 95 % uncertainty ranges can be tested by drawing a large number of samples of possible parameter values from that distribution, and for each drawing a measurement at random according to the measurement uncertainty distribution and estimating a range for the parameter.

Large sample depth during other periods is not then required (a robust curve to remove growth trend is already in hand) except in relation to the general coherence of the tree response to the environmental parameter of interest — if the trees are «noisy» then a larger number of samples will be required to extract signal than if the signal is more coherent.

The hope, of course, is that enough parameters are being used to do a decent job and that the large number of cells will let the errors wash out.

Each of the multiple models that produced that hindcast has a large number of alternative parameter sets.

As best I can tell from work with ensembles of models with perturbed parameters, there is no reason to assume that any scheme for tuning parameters one at a time produces anything better than local optimum in a large multi-dimensional parameter space with a large number of such optima.