Sentence examples for a steady probability distribution from inspiring English sources

The latter is only evident when the matrix M is recursively multiplied by the vector <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0003626.e012.PNG" class= inline-graphic"/> containing the fraction of cells per attractor, ideally until the system reaches a steady probability distribution.

We mainly focus our research on two aspects, i.e., the dynamical switch characters (including the steady probability distribution, the mean value and the mean first passage time) and the stochastic resonance phenomenon.

With σ = 0.5 mL/min, the condition for the existence of a steady-state probability distribution is met with ease.

The steady-state probabilities can be solved from the Chapman-Kolmogorov equation (dot{pi }={pi }) Q at steady-state ({pi }) Q (,= 0, ;; sum _{i=0}^{N+1} pi _i = 1), where Q is the rate transition matrix, as explicitly expressed in Appendix A. Since the model in Fig. 1a is of finite state and irreducible, a unique steady state probability distribution exists [24].

It is the same as the steady state probability distribution function if the steady state exists.

Intervention strategies are applied to drive the whole genetic network into a desirable steady-state probability distribution.

In this study, we illustrate an approach of determining the steady state probability distribution efficiently to carry on continuation in multiple variables within a large-scale parameter screen.

We are now in a position to calculate the steady state probability distribution (P_{0}(theta)) and use this to calculate the moments of the phase dynamics.

In contrast to the previous studies, here we use a truncated state-space for steady state probability distribution approximation, which is arguably more general since we make no assumptions on the relationships of the moments of the distribution.

It takes 1,341 seconds (about 23 minutes) of CPU time to compute the steady state probability distribution on a Linux workstation.

In order to ameliorate the computational cost and complexity, we present a method here to approximate the steady state probability distribution by 1) reducing the system's state-space to a finite dimension using truncated state-space method [ 15] and 2) subsequently, translating an eigenvalue problem associated with a CME to a system of linear equations.