Sentences with phrase «number of degrees of freedom of»

is Avogadro's number — you could use the molecular heat capacity and insert the number of degrees of freedom of the gas and write this whole thing in a different form with the temperature explicitly inserted, but it is not zero.

The probable release of water molecules from the hydration layer of ions caused by cluster formation may result in an increased number of degrees of freedom of the system.

Not exact matches

However improbable in a mechanistic sense the elaborate organic structure created by life may appear, it seems increasingly evident that the cosmic substance is drawn toward these states of extreme arrangement by a particular kind of attraction which compels it, by the play of large numbers in which it is involved, to miss no opportunity of becoming more complex and thus achieving a higher degree of freedom.

But the attempt to reduce living systems to such, that is to say formal reductionism, fails in part because the number of possible combinations or classifications is generally immensely larger than the number of degrees of freedom.

One important step in understanding a physical system consisting of a large number of entities — for example, the atoms making up a magnetic material — is to identify among the many degrees of freedom of the system those that are most relevant for its physical behaviour.

DAC works by reducing degrees of freedom, the number of values that encode motion, to speed up simulations while still capturing important motions for dynamic scenarios.

The number of degrees of freedom is adequate.

Finally, because of the high amount of randomness in year - to - year returns (single years), the data have an adequate number of degrees of freedom.

Making things more difficult, stock returns are highly correlated from one year to the next, reducing the effective number of degrees of freedom.

The effective number of degrees of freedom is reduced because of overlapping data.

y = HSWR80 Calculated Rate (percent) and x = percentage earnings yield = 100 / [P / E10] 1923 - 1930 y = 0.5515 x + 2.5346 1923 - 1940 y = 0.5274 x + 2.3765 1923 - 1950 y = 0.6276 x + 2.2028 1923 - 1960 y = 0.6473 x + 2.1637 1923 - 1970 y = 0.7312 x + 1.379 1923 - 1980 y = 0.6685 x + 1.6424 y = HSWR80 Calculated Rate (percent) and x = percentage earnings yield = 100 / [P / E10] 1931 - 1940 y = 0.4456 x + 2.7071 1931 - 1950 y = 0.7189 x + 1.5714 1931 - 1960 y = 0.7459 x + 1.5098 1931 - 1970 y = 0.8419 x + 0.6639 1931 - 1980 y = 0.7117 x + 1.3346 I scaled my previous confidence limits of 1.58 % (for HSWR80) by taking the ratio of the Student t test confidence limit for a given number of freedom to that with 60 degrees of freedom.

Since then games which follow Descent's formula are very few in number, but now developers Sigtrap Games along with publishers Mastertronic have revitalized the Descent gameplay style with their Rouge - like 6 degrees of freedom action shooter: Sublevel Zero.

The error bars I gave were 95 % including an adjustment for temporal autocorrelation (which reduces the degrees of freedom)-- M&W's number is the 1 sigma, with no correction.

c now determine suggested number of EOFs in training c based on rule N applied to the proxy data alone c during the interval t > iproxmin (the minimum c year by which each proxy is required to have started, c note that default is iproxmin = 1820 if variable c proxy network is allowed (latest begin date c in network) c c we seek the n first eigenvectors whose eigenvalues c exceed 1 / nproxy» c c nproxy» is the effective climatic spatial degrees of freedom c spanned by the proxy network (typically an appropriate c estimate is 20 - 40)

To test that I varied the data sources, the time periods used, the importance of spatial auto - correlation on the effective numbers of degree of freedom, and most importantly, I looked at how these methodologies stacked up in numerical laboratories (GCM model runs) where I knew the answer already.

Foster and Rahmstorf deal with autocorrelation in an appendix to their paper by considering the reduction in the number of effective of degrees of freedom.

I wonder if there is a non-equilibrium quasi steady state non-reproducible thermodynamic system, one with a vast number of internal degrees of freedom (other than the terrestrial climate system), which is successfully described by a computational model.

[Response: There are a couple of issues here — first, the number of ensemble members for each model is not the same and since each additional ensemble member is not independent (they have basically the same climatological mean), you have to be very careful with estimating true degrees of freedom.

The answer is that while the number of explicit or implicit input parameters into a GCM is in the hundreds at least, the number of degrees of freedom in the output model is enormous.

What I'm trying to get at is that although the problem with records is clearly the arbitrarily large number of degrees of freedom that they invoke.

«What is generally required [for proving solar forcing of climate change] is a consistent signal over a number of cycles (either the 11 year sunspot cycle or more long term variations), similar effects if the timeseries are split, and sufficient true degrees of freedom that the connection is significant and that it explains a non-negligible fraction of the variance.»

But it's easy to calculate the probability of seeing a number of new records from binominal law if you know the true degrees of freedom.

[Response: Estimates of the error due to sampling are available from the very high resolution weather models and from considerations of the number of degrees of freedom in the annual surface temperature anomaly (it's less than you think).

This will further improve the number of degrees of freedom in the image of F3.

Only the number of degrees of freedom counts when judging questions of overfitting etc..

Leif Svalgaard, wrote [of Le Mouël, Blanter, Shnirman, & Courtillot (2010)-RSB-, «Second, the data is heavily smoothed [4 - year sliding window which reduces the number of degrees of freedom enormously and makes the data points very dependent on each other].»

Second, the data is heavily smoothed [4 - year sliding window which reduces the number of degrees of freedom enormously and makes the data points very dependent on each other].

My current plan is to double F3's frequency (which will double the number of degrees of freedom in the image of F3 assuming the same SNR) while cutting back on parameters, maybe down to 6.

Did his justification change the number of effective degrees of freedom used in deciding to subtract 1 from Wien's denominator?

There should be a relationship between the number of degrees of freedoms the model uses and the validation tests that determine whether it is skillful or not.

In summary: models are not set up to be skillful for a number of degrees of freedom, they are designed to be skillful at a particular scale and time horizon.

Firstly, they increase the number of degrees of freedom by moving from 30 - year smoothing to decadal tests.

When we came out with a number from the fit, there were chi square per degrees of freedom and error bars + / - statistical, + / - systematic.

I'm not really that familiar with the efforts that have been made to validate the hindcast of global climate models, BUT if they are skillful with respect to the number of degrees of freedom they use and predict, then they are skillful.

The number of degrees of freedom in any model or theory of the climate system far swamps that of sub-atomic particles or genomes.

The number of degrees of freedom is associated with the test vector with which the models are validated, and defines how challenging the test is.

A model can be a good model given a certain amount of degrees of freedom and not at another number of degrees of freedom, but if the model is based on the first amount of degrees of freedom it can still be a good model.

Clearly, like Spence UK rightly said, the number of the degrees of freedom will be large.

And as soon as we increase the number of degrees of freedom, the models fail miserably (e.g. Anagnostopoulos 2010).

The other problem is a mathematical one, in terms of how you actually evaluate with observations a model with a very large number of degrees of freedom that is nonlinear / chaotic as well.

Owing to the decreased number of spatial degrees of freedom in the earliest reconstructions (associated with significantly decreased calibrated variance before e.g. 1730 for annual - mean and cold - season, and about 1750 for warm - season pattern reconstructions) regional inferences are most meaningful in the mid 18th century and later, while the largest - scale averages are useful further back in time.

The point that systematic error propagates as sqrt -LSB-(sum - over-scatter) ^ 2 / (N - 1)-RSB--- where N is the number of measurements — follows from the fact that a degree of freedom is lost through the use of the mean measurement in calculating the systematic scatter.

The important point in this statistical law is that if we add some energy to a great number of molecules, this energy will be shared equally among their translational, rotational and vibrational degrees of freedom.

σw is the raw OLS estimate of the standard uncertainty, ν is the ratio of the number of observations to the number of independent degrees of freedom, and σc is the corrected standard uncertainty.

That way you might be able to understand how the heat capacity at constant volume is related to the number of degrees of freedom available to store heat at the molecular level (instead of stating that it doesn't depend on them, which is simply incorrect).

In either case, the prediction error reduces to zero when the maximum number of PLS components is used (one less than the number of models), since then there are sufficient degrees of freedom available to exactly fit each CMIP5 model's predictand.

The number of degrees of freedom?

Of course with the number of degrees of freedom you have introduced the fit looks good but to show an extrapolation of the fit is absurd and not a mistake I would expect of an undergraduatOf course with the number of degrees of freedom you have introduced the fit looks good but to show an extrapolation of the fit is absurd and not a mistake I would expect of an undergraduatof degrees of freedom you have introduced the fit looks good but to show an extrapolation of the fit is absurd and not a mistake I would expect of an undergraduatof freedom you have introduced the fit looks good but to show an extrapolation of the fit is absurd and not a mistake I would expect of an undergraduatof the fit is absurd and not a mistake I would expect of an undergraduatof an undergraduate.

I have seen corrections using a reduction in the number of degrees of freedom in calculating confidence intervals due to autocorrelations of data, but I do not have a finger on how or when to make the correction.

Using the method detailed in Ripley (1987) and Neal (1993) we find the number of degrees of freedom (df) to be 4 in the CR flux dataset and 7 in the globally averaged low cloud dataset.