Time Evolution Of Probability Distribution Research Articles

Nonlinear effective dynamics of a Brownian particle in magnetized plasma

An effective description is presented for a Brownian particle in a magnetized plasma. In order to systematically capture various corrections to linear Langevin equation, we construct effective action for the Brownian particle, to quartic order in its position. The effective action is first derived within nonequilibrium effective field theory formalism, and then confirmed via a microscopic holographic model consisting of an open string probing magnetic fifth-dimensional anti--de Sitter (${\mathrm{AdS}}_{5}$) black brane. For practical usage, the non-Gaussian effective action is converted into Fokker-Planck-type equation, which is an Euclidean analog of Schr\"odinger equation and describes time evolution of probability distribution for particle's position and velocity.

Statistical Mechanics of Long Walks in Dynamic Complex Networks: Statistical Arguments for Diversifying Selection

We study the thermodynamic limit of very long walks on finite, connected, non-random graphs subject to possible random modifications and transportation capacity noise. As walks might represent the chains of interactions between system units, statistical mechanics of very long walks may be used to quantify the structural properties important for the dynamics of processes defined in networks. Networks open to random structural modifications are characterized by a Fermi–Dirac distribution of node’s fugacity in the framework of grand canonical ensemble of walks. The same distribution appears as the unique stationary solution of a discrete Fokker–Planck equation describing the time evolution of probability distribution of stochastic processes in networks. Nodes of inferior centrality are the most likely candidates for the future structural changes in the network.

Weak Stability of Centred Quadratic Stochastic Operators

We consider the weak convergence of iterates of so-called centred quadratic stochastic operators. These iterations allow us to study the discrete time evolution of probability distributions of vector-valued traits in populations of inbreeding or hermaphroditic species, whenever the offspring’s trait is equal to an additively perturbed arithmetic mean of the parents’ traits. It is shown that for the existence of a weak limit, it is sufficient that the distributions of the trait and the perturbation have a finite variance or have tails controlled by a suitable power function. In particular, probability distributions from the domain of attraction of stable distributions have found an application, although in general the limit is not stable.

Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks

A sparse parameter matrix estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. Stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation (CME) describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation can be derived. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized. A simulation result is provided to verify performance improvement of regularized MLE over least-square estimation (LSE), which is based on a deterministic mass-average model, in the case of a small population size.

Regularized Maximum Likelihood Estimation of Sparse Stochastic Monomolecular Biochemical Reaction Networks

A sparse parameter estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. Stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation, which is composed of several ordinary differential equations describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation is available. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized, whereas most of existing studies on sparse reaction network identification use deterministic models for regularized least-square estimation. A simulation result is provided to verify performance improvement of the presented regularized MLE over the least squares (LSE) based on a deterministic mass-average model in the case of a small population size. Improved reaction structure detection is achieved by adding a penalty term for ℓ1 regularization to the exact maximum likelihood function.

Invariant Markov processes on compact groups and correlation functions

Random processes governing the time evolution of probability distributions of many physical systems can be described by continuous homogeneous Markov processes taking values from compact groups. Assuming the transition probability function of the process to be invariant in the sense that P t ( x, B) = P t ( e, x -1 B) with e being the neutral element of the group, the harmonic analysis (Weyl theory) is applied to study the properties of the Markov semi-group. The infinitesimal operator and generating functional are decomposed using the Levy-Khinchin formula. Under some auxiliary assumptions the components of this decomposition are interpreted as generators of a one parameter subgroup, Brownian motion and a jump process. The formalism is illustrated for several models of processes taking values from compact Lie groups. The properties of the correlation functions of time dependent random variables are investigated.

Dynamics of probability distributions over classical fields

It is shown that the original Hopf formulation for the time evolution of probability distributions over classical fields follows deductively from the space-time version of the theory.