The effect of strongly anisotropic turbulent mixing on critical behavior: Renormalization group analysis of two nonstandard systems

We consider the effect of strongly anisotropic turbulent mixing on the critical behavior of two systems: a φ ³ critical dynamics model describing universal properties of metastable states in the vicinity of a firstorder phase transition and a reaction-diffusion system near the point of a second-order transition between fluctuation and absorption states (a simple epidemic process or the Gribov process). In both cases, we demonstrate the existence of a new strongly nonequilibrium, anisotropic scaling regime (universality class) for which both the mixing and the nonlinearity in the order parameter are relevant. We evaluate the corresponding critical dimensions in the one-loop approximation of the renormalization group.     

Continuous Markovian Model for Unexpected Shift in SPC

Various process models for discrete manufacturing systems (parts industry) can be treated as bounded discrete-space Markov chains, completely characterized by the original in-control state and a transition matrix for shifts to an out-of-control state. The present work extends these models by using a continuous-state Markov chain, incorporating non-random corrective actions. These actions are to be realized according to the statistical process control (SPC) technique and should substantially affect the model. The developed stochastic model yields Laplace distribution of a process mean. Real-data tests confirm its applicability for the parts industry and show that the distribution parameter is mainly controlled by the SPC sample size.     

Examples of fitting structured phase–type distributions

A sub–class of phase–type distributions is defined in terms of a Markov process with sequential transitions between transient states and transitions from these states to absorption. Such distributions form a very rich class; they can be fitted to data, and any structure revealed by the parameter estimates used to develop more parsimonious re–parametrizations. Several example data sets are used as illustrations.     

Standard tri-point transition function

It is usually difficult to express a family of tri-point transition function (TTF) by a transition matrix as Markov processes with one parameter. In this paper, we define three kinds of connection matrixes on the states of standard tri-point transition function (STTF) and study their essential character, give a constructive method on the constant-value standard tri-point transition function and a general expression of the state-symmetric standard tri-point transition function by a sequence of the transition matrixes of special and simple Markov processes with one parameter.     

Markov cubature rules for polynomial processes

We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rules. The polynomial property allows us to study such rules using algebraic techniques. Markov cubature rules aid the tractability of path-dependent tasks such as American option pricing in models where the underlying factors are polynomial processes.     

Studying an overload system using rotation

1. IntroductionThe motivation of writing this paper was from calculating the blocking probability foran overloaded finite system. Our numerical experiments suggested that this probability canbe approximated efficiently by rotating the transition matrix by 180". Some preliminaryresults were obtained and can be found in [11 and [2]. Rotating the transition matrix definesa new Markov chain, which is often called the dual process in the literature, for example,[3--7]. For a finite Markov chain, …     

基于马尔科夫过程的储备系统稳态性能分析

由于储备系统组成部件在存储期间的失效概率各不相同,当部件状态趋于稳定时,各个状态对系统性能的影响也存在差异。为了识别关键部件及其状态对系统性能的影响程度,本文以重要度为主要指标,应用马尔科夫过程研究储备系统在稳态时的性能变化模式。首先基于综合重要度研究系统性能的变化规律,并结合冷储备系统和温储备系统的状态转移矩阵推导出马尔科夫过程中稳态值的计算方法;其次基于稳态综合重要度获得系统稳态时的性能变化模式;最后以双臂机器人为例,分析部件处于不同状态时对系统性能的影响模式,比较了不同部件综合重要度的变化,验证了提出方法的有效性。     

Robust Markov perfect equilibria

In this paper we study a Markov decision model with quasi-hyperbolic discounting and transition probability function depending on an unknown parameter. Assuming that the set of parameters is finite, the sets of states and actions are Borel and the transition probabilities satisfy some additivity conditions and are atomless, we prove the existence of a non-randomised robust Markov perfect equilibrium.     

A structural model of the higher‐order Markov process incorporating reversion effects

The paper presents a formal approach which may increase the realism and parsimony of higher‐order Markov models applied to certain human behaviors. Often in behavioral applications, any improvements in fit available from increasing the order of a Markov model would be more than offset by interpretive problems caused by the very rapid increase in the number of independent parameters. The model proposed here for the higher‐order process greatly reduces the number of independent parameters, replacing them with sociologically relevant effects of persistence in and reversion to previous conditions.

The general model is called the “reversion model.” In it, individuals are allowed to carry along some information about their pasts, for a number of periods corresponding to the order of the model. The parameters describing residence histories are constructed to give each individual an underlying set of first‐order transition probabilities, which are modified by experience of the various states of the system. When an individual occupies a particular state, his relative probability of future residence there (vis‐a‐vis the other states as a group) is permitted to change. But occupation of a particular state is not permitted to affect the relative chances of residence among the other states. With suitable constraints, the number of parameters of this higher‐order process no longer increases geometrically with the order, but only arithmetically.

Maximum likelihood estimation formulas are derived for the reversion model, which is then applied to longitudinal data on the work activities of U.S. Ph.D. physicists and chemists in 1960–1966, and is found to fit well using likelihood ratio tests.     

Asymptotic solutions of the Cahn-Hilliard system in the fast relaxation case

The paper deals with the construction and justification of asymptotic solutions to the Cahn-Hilliard system, which models the separation of phases in metastable equilibrium (crystallization processes). The authors construct an asymptotic solution that describes the dynamics of the interface, i.e., the surface where the phase transition occurs. It is shown that the solution obtained in the limit, as the small parameter tends to zero, satisfies a modified crystallization system with a generalized Gibbs-Thomson condition on the interface. Bibliography: 27 titles. Translated from Trudy Seminara imeni I. G. Petrovskogo, No. 20, pp. 48–80, 1997.     

Ergodic and adaptive control of hidden Markov models

A partially observed stochastic system is described by a discrete time pair of Markov processes. The observed state process has a transition probability that is controlled and depends on a hidden Markov process that also can be controlled. The hidden Markov process is completely observed in a closed set, which in particular can be the empty set and only observed through the other process in the complement of this closed set. An ergodic control problem is solved by a vanishing discount approach. In the case when the transition operators for the observed state process and the hidden Markov process depend on a parameter and the closed set, where the hidden Markov process is completely observed, is nonempty and recurrent an adaptive control is constructed based on this family of estimates that is almost optimal.     

On One Property of a Regular Markov Chain

We prove that if a certain row of the transition probability matrix of a regular Markov chain is subtracted from the other rows of this matrix and then this row and the corresponding column are deleted, then the spectral radius of the matrix thus obtained is less than 1. We use this property of a regular Markov chain for the construction of an iterative process for the solution of the Howard system of equations, which appears in the course of investigation of controlled Markov chains with single ergodic class and, possibly, transient states.     

Switching diffusion logistic models involving singularly perturbed Markov chains: Weak convergence and stochastic permanence

Focusing on stochastic dynamics involve continuous states as well as discrete events, this article investigates stochastic logistic model with regime switching modulated by a singular Markov chain involving a small parameter. This Markov chain undergoes weak and strong interactions, where the small parameter is used to reflect rapid rate of regime switching among each state class. Two-time-scale formulation is used to reduce the complexity. We obtain weak convergence of the underlying system so that the limit has much simpler structure. Then we utilize the structure of limit system as a bridge, to invest stochastic permanence of original system driving by a singular Markov chain with a large number of states. Sufficient conditions for stochastic permanence are obtained. A couple of examples and numerical simulations are given to illustrate our results.     

BMAP的轨道分析和Q过程的伴随MAP

本文用轨道分析方法研究批量Markov到达过程(BMAP),有别于研究BMAP常用的矩阵解析方法.通过BMAP的表现(D_k,k=0,1,2,…),得到BMAP的跳跃概率,证明了BMAP的相过程是时间齐次Markov链,求出了相过程的转移概率和密度矩阵.此外,给定一个带有限状态空间的Q过程J,其跳跃点的计数过程记为N,证明了Q过程J的伴随过程X*=(N,J)是一个MAP,求出了该MAP的转移概率和表现(D_0,D_1),它们是通过密度矩阵Q来表述的.     

连续时间马氏链中的两个计数过程

木文考虑连续时间齐次Markov链在(O,t]期间状态转移次数和从状态集A到B的转移次数.为计算平均转移次数,我们得到了某些在随机模型中极其有用的简便公式并引进了无限位相型(Phase Type)分布.     

Gibbs paradox, liquid phase as an alternative to the bose condensate, and homogeneous mixtures of new ideal gases

In this paper, relations for the liquid phase of a new ideal gas are given. The points of the spinodal of the liquid phase (i.e., the endpoints of metastable states) in the domains of positive and negative pressures are defined. Relations for the critical values of homogeneous mixtures are presented. We study the phase transition and critical indices from the point of view of the geometric quantization of thermodynamics.     

Multiscale diffusion approximations for stochastic networks in heavy traffic

Stochastic networks with time varying arrival and service rates and routing structure are studied. Time variations are governed by, in addition to the state of the system, two independent finite state Markov processes X and Y. The transition times of X are significantly smaller than typical inter-arrival and processing times whereas the reverse is true for the Markov process Y. By introducing a suitable scaling parameter one can model such a system using a hierarchy of time scales. Diffusion approximations for such multiscale systems are established under a suitable heavy traffic condition. In particular, it is shown that, under certain conditions, properly normalized buffer content processes converge weakly to a reflected diffusion. The drift and diffusion coefficients of this limit model are functions of the state process, the invariant distribution of X, and a finite state Markov process which is independent of the driving Brownian motion.     

A Markov Renewal Approach to M/G/1 Type Queues with Countably Many Background States

We consider the stationary distribution of the M/GI/1 type queue when background states are countable. We are interested in its tail behavior. To this end, we derive a Markov renewal equation for characterizing the stationary distribution using a Markov additive process that describes the number of customers in system when the system is not empty. Variants of this Markov renewal equation are also derived. It is shown that the transition kernels of these renewal equations can be expressed by the ladder height and the associated background state of a dual Markov additive process. Usually, matrix analysis is extensively used for studying the M/G/1 type queue. However, this may not be convenient when the background states are countable. We here rely on stochastic arguments, which not only make computations possible but also reveal new features. Those results are applied to study the tail decay rates of the stationary distributions. This includes refinements of the existence results with extensions.     

Markov chain inversion approach to identify the transition rates of ion channels

We consider how to identify the transition rates of ion channels with the underlying scheme which is kinetically modelled as time-homogeneous Markov chain. A Markov chain inversion approach is developed to perform a difficult inversion to identify the transition rates from the parameters characterizing the lifetime distributions at a small number of states, although it is straightforward to derive the lifetime distribution. The general explicit equations relating the parameters of the lifetime distribution to the transition rates are derived and transition rates are then obtained as roots to this system of equations. The concrete solutions are proposed to the basic and regular schemes such as linear, star-graph branch and loop. Useful conclusions and solutions to realistic schemes are also included to show its efficiency.