Large Deviations for Empirical Measures of Not Necessarily Irreducible Countable Markov Chains with Arbitrary Initial Measures

All known results on large deviations of occupation measures of Markov processes are based on the assumption of （essential） irreducibility. In this paper we establish the weak＊ large deviation principle of occupation measures for any countable Markov chain with arbitrary initial measures. The new rate function that we obtain is not convex and depends on the initial measure, contrary to the （essentially） irreducible case.     

Markov过程的一致中偏差──离散参数情形

本文证明：离散参数Ｍａｒｋｏｖ过程的一致中偏差原理成立的充要条件是Ｄｏｅｂｌｉｎ常返性（即：满足Ｄｏｅｂｌｉｎ条件且是Ｈａｒｒｉｓ常返的）。     

Large Deviations for the Markov Binomial Distribution

Considering the Markov binomial distribution, we study large deviations for the Poisson approximation. Apart from the standard choice of parameters, we use the approach where the parameter of approximation depends on the argument of the approximated distribution function.     

Ratio ergodicity for non-homogeneous Markov Chains in general state spaces

In Refs. 7, 8, 12–14, applications of Birkhoff's coefficient of ergodicity to the study of products of finite and infinite matrices are given. In this paper we intend to show that using the ideas and results of Birkhoff and Baueret al. one can establish such results in the general setting of positive operators on a space of measures and in particular, in the study of nonhomogeneous Markov Chains in general state spaces. In this setup the proofs are very simple. Since the high level of generality in Ref. 1 hinders an easy understanding of the special case of interest to probabilists, we present a self-contained treatment. Even though our aim is methodological, our theorems are new, even in the case of infinite matrices.Support from the NSERC grant of Professor Rosenberg is gratefully acknowledged.Research supported by an NSERC grant.     

三维Wiener Sausage体积的中偏差

令{β(s),s≥0}表示R~3空间中的标准Brown运动,|W_r(t)|表示由{β(s),s≥0}产生的观察至时间t且以r为半径的Wiener sausage的体积.由中心极限定理可知,(|W_r(t)|-E|W_r(t)|)/(?)弱收敛至正态分布.本文研究这种情况下的中偏差.     

Large Deviations for Symmetrised Empirical Measures

In this paper we prove a Large Deviation Principle for the sequence of symmetrised empirical measures \(\frac{1}{n}\sum_{i=1}^{n}\delta_{(X^{n}_{i},X^{n}_{\sigma_{n}(i)})}\) where σ _n is a random permutation and ((X _i ⁿ )_1≤i≤n ) _n≥1 is a triangular array of random variables with suitable properties. As an application we show how this result allows to improve the Large Deviation Principles for symmetrised initial-terminal conditions bridge processes recently established by Adams, Dorlas and König.     

Moderate Deviations for Random Sums of Heavy-Tailed Random Variables

Let {Xn;n≥ 1} be a sequence of independent non-negative random variables with common distribution function F having extended regularly varying tail and finite mean μ = E（X1） and let {N（t）; t ≥0} be a random process taking non-negative integer values with finite mean λ（t） = E（N（t）） and independent of {Xn; n ≥1}. In this paper, asymptotic expressions of P（（X1 ＋… ＋XN（t）） -λ（t）μ 〉 x） uniformly for x ∈[γb（t）, ∞） are obtained, where γ〉 0 and b（t） can be taken to be a positive function with limt→∞ b（t）/λ（t） = 0.     

Moderate Deviations for Longest Increasing Subsequences: The Lower Tail

We derive a moderate deviation principle for the lower tail probabilities of the length of a longest increasing subsequence in a random permutation. It refers to the regime between the lower tail large deviation regime and the central limit regime. The present article together with the upper tail moderate deviation principle in Ref. 12 yields a complete picture for the whole moderate deviation regime. Other than in Ref. 12, we can directly apply estimates by Baik, Deift, and Johansson, who obtained a (non-standard) Central Limit Theorem for the same quantity.     

An Asymptotic Expansion for Probabilities of Moderate Deviations for Multivariate Martingales

We derive formulae for probabilities of large deviations in a moderate range for multivariate martingales. Although we give an elementary proof for univariate martingales, there is no elementary extension to the multivariate case. The hard point is to produce a proper estimate for the norming factor. For this we develop a method of sequential projectors which allows us to obtain the desired natural extension of the result in the univariate case.     

齐次马氏链强弱遍历等价关系的一种证明

主要给出齐次马氏链强遍历和弱遍历等价关系的一个直接证明.     

随机有序马尔可夫链收敛速度显式界的注记

弱化Scott与Tweedie在计算马氏链收敛速度界时的条件,即变一步转移概率为m(m≥1)步转移概率,并运用不同于Scott与Tweedie的方法,计算出马氏链几何收敛速度r~n的界,从而推广了已有的结论.     

Ergodic Theorems and Ergodic Decomposition for Markov Chains

This paper considers Markov chains on a locally compact separable metricspace, which have an invariant probability measure but with no otherassumption on the transition kernel. Within this context, the limit providedby several ergodic theorems is explicitly identified in terms of the limitof the expected occupation measures. We also extend Yosidasergodic decomposition for Feller-like kernels to arbitrarykernels, and present ergodic results for empirical occupation measures, aswell as for additive-noise systems.     

Quasi-Feller Extensions of Markov Chains and Existence of Dual Chains

The author presents a revised and detailed version of his theorem on the existence of Feller's extensions of Markov chains; to this end the broader notion of quasi-Feller extension is used. The existence of Markov chains dual to the chains with Borel space of states is derived from this result. Chains irreducible in the Orey sense are studied in most detail. For example, we prove that for such chains the quasi-Feller extension can be chosen recurrent or Liouville if the original chains possess these properties.     

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In this paper, we introduce the definitions of geometric strongly ergodic, strongly ergodic and weakly ergodic for continuous-state Markov chains, then we give a primary proof of equivalence of the ergodicities for continuous-state Markov chains.     

Moderate deviations for time-varying dynamic systems driven by non-homogeneous Markov chains with Two-time Scales

Motivated by problems arising in time-dependent queues and dynamic systems with random environment, this work develops moderate deviations principles for dynamic systems driven by a fast-varying non-homogeneous Markov chain in continuous time. A distinct feature is that the Markov chain is time dependent or inhomogeneous, so are the dynamic systems. Under irreducibility of the non-homogeneous Markov chain, moderate deviations of a non-homogeneous functional are established first. With the help of a martingale problem formulation and a functional central limit theorem for the two timescale system, both upper and lower bounds of moderate deviations are obtained for the rapidly fluctuating Markovian systems. Then applications to queueing systems and dynamic systems modulated by a fast-varying Markov chain are examined.     

肖争艳等： 绕积马氏链的状态分类

该文给出了绕积马氏链的特征数和状态的定义, 利用一般马氏链的理论讨论了随机环 境中的马氏链的各种状态的特征以及各类状态之间的联系, 还给出了在联合空间不可分解且 正则本质的条件下, 状态正则本质的充要条件. 最后举例说明了经典马氏链和随机环境中马氏链的状态的区别.     

Quasi-Stationarity of Discrete-Time Markov Chains with Drift to Infinity

We consider a discrete-time Markov chain on the non-negative integers with drift to infinity and study the limiting behavior of the state probabilities conditioned on not having left state 0 for the last time. Using a transformation, we obtain a dual Markov chain with an absorbing state such that absorption occurs with probability 1. We prove that the state probabilities of the original chain conditioned on not having left state 0 for the last time are equal to the state probabilities of its dual conditioned on non-absorption. This allows us to establish the simultaneous existence, and then equivalence, of their limiting conditional distributions. Although a limiting conditional distribution for the dual chain is always a quasi-stationary distribution in the usual sense, a similar statement is not possible for the original chain.     

Bounds on regeneration times and convergence rates for Markov chains

In many applications of Markov chains, and especially in Markov chain Monte Carlo algorithms, the rate of convergence of the chain is of critical importance. Most techniques to establish such rates require bounds on the distribution of the random regeneration time T that can be constructed, via splitting techniques, at times of return to a “small set” C satisfying a minorisation condition P(x,·)(·), xC. Typically, however, it is much easier to get bounds on the time τ_C of return to the small set itself, usually based on a geometric drift function , where . We develop a new relationship between T and τ_C, and this gives a bound on the tail of T, based on ,λ and b, which is a strict improvement on existing results. When evaluating rates of convergence we see that our bound usually gives considerable numerical improvement on previous expressions.     

Transportation inequalities for hidden Markov chains and applications

We prove some transportation inequalities for hidden Markov chains, generalize the results proved by Kontorovich and Ramanan in two directions and give some applications to log-likelihood functions and hypothesis testing.     

随机环境中的马氏链状态分类

给出了随机环境中马氏链的特征数和状态的定义,讨论了状态间的传递性,自反性,对称性,是经典马氏链相应结果的一般化.并运用位势方法研究了状态间的关系,它们在极限理论的研究中非常有用.