Sentences with phrase «stochastic dynamics»

It was in fact Hurst who studied Nile River flows with a methodology that has become known as Hurst - Kolmogorov stochastic dynamics.

But it suggests — there's that abductive inference word again — that the data should be analysed for Hurst - Kolmogorov stochastic dynamics.

Furthermore, we characterize this variability over the widest possible range of scales that the available information allows, and we try to connect the deterministic Milankovitch cycles with the Hurst — Kolmogorov (HK) stochastic dynamics.

Rather it warns us to change our perception of natural processes as resembling these simple idealized mathematical processes and to move towards a new type of stochastic dynamics.

Not exact matches

By building a physical framework for this motion, we can identify key factors in the nuclear structure and function as well as expand the field of stochastic polymer dynamics.

The study found that the PDMP mathematical model accurately describes the random, or stochastic, dynamics of gene expression in the non-adiabatic regime (where promoter kinetics are slow and fast averaging can not occur).

Paper: Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes, Yen Ting Lin and Nicolas E. Buchler, Royal Society Interface volume and date

Although this index is based on the limited number of years (1997 - 2010) for which daily snow cover data exists, the high correlation between the SAI and the DJF AO suggests that the wintertime AO is indeed predictable, rather than a by - product of the stochastic behavior of internal atmospheric dynamics.

-- Do stochastic predictions disregard deterministic dynamics in all time horizons?

Unless a stochastic framework is used, neglecting deterministic dynamics in long - term prediction is preferable.

I propose the following Climatic null hypothesis that: «Natural climatic variation is quantified by the stochastic uncertainty envelope of historical and paleo data, embodying the nonlinear chaotic interaction of atmospheric, oceanic, volcanic, solar, and galactic processes, including climate persistence quantified by Hurst - Kolmogorov dynamics.»

«Natural climatic variation is quantified by the stochastic uncertainty envelope of historical and paleo data, embodying the nonlinear chaotic interaction of atmospheric, oceanic, volcanic, solar, and galactic processes, including climate persistence quantified by Hurst - Kolmogorov dynamics.»

In a recent technical comment, Zhang et al. show that ocean dynamics play a central role in the Atlantic Multidecadal Oscillation (AMO), and the previous claims that «the AMO is a thermodynamic response of the ocean mixed layer to stochastic atmospheric forcing, and ocean circulation changes have no role in causing the AMO» are not justified.

There is much progress on the cloud microphysics front; with regard to the cloud dynamics issue, it seems that stochastic parameterizations are the way to go.

Concerning the model dynamics, I'm very much concerned by the fact that the models are not stochastic.

The role of stochastics is then even more crucial: (a) to infer dynamics (laws) from past data; (b) to formulate the system equations; (c) to estimate the involved parameters; and (d) to test any hypothesis about the dynamics.

The combination of complex deterministic dynamics and stochastic disturbances may be similar to fundamental chaotic behavior over long periods, but there are differences.

Nonlinear dynamics, threshold effects and surprise are common in highly modified systems due to cumulative human impacts, trophic cascades and stochastic effects [25].

Use stochastic models that include climate persistence (stochastic Hurst Kolmogorov dynamics) to improve understanding and predictability.

The time scale of variability of the patterns is longer than the decorrelation time scale of the stochastic forcing, because of the temporal integration of the forcing by the equations of motion limited by the effects of nonlinear dynamics and friction.

This paper illustrates a method for operationalizing affect dynamics using a multilevel stochastic differential equation (SDE) model, and examines how those dynamics differ with age and trait - level tendencies to deploy emotion regulation strategies (reappraisal and suppression).