马尔可夫区间映射中广义非适应测度的熵密度

Ergodic properties of nonhomogeneous Markov chains defined on ordered Banach spaces with a base

It is known that the Dobrushin's ergodicity coefficient is one of the effective tools to study a behavior of non-homogeneous Markov chains. In the present paper, we define such an ergodicity coefficient of a positive mapping defined on ordered Banach space with a base (OBSB), and study its properties. In terms of this coefficient we prove the equivalence uniform and weak ergodicities of homogeneous Markov chains. This extends earlier results obtained in case of von Neumann algebras. Such a result allowed to establish a category theorem for uniformly ergodic Markov operators. We find necessary …

All properly ergodic Markov chains over a free group are orbit equivalent

Previous work showed that all Bernoulli shifts over a free group are orbit-equivalent. This result is strengthened here by replacing Bernoulli shifts with the wider class of properly ergodic countable state Markov chains over a free group. A list of related open problems is provided.

一般准则下非齐次马尔可夫过程的遍历性

Ergodicity of inhomogeneous Markov processes under general criteria

本文针对非齐次马尔可夫过程，通过推广Krylov-Bogolyubov方法或采用Lyapunov准则建立不变测度族的存在性，并在压缩假设下证明唯一性与指数遍历性，以三个实例验证方法的实用性。

Markov Stochastic Operators of Heredity

Applying non-ergodic quadratic stochastic operator the continual family of weak ergodic non-homogeneous Markov chains is constructed.

On the rate of convergence to equilibrium for countable ergodic Markov chains

Using elementary methods, we prove that for a countable Markov chain $P$ of ergodic degree $d > 0$ the rate of convergence towards the stationary distribution is subgeometric of order $n^{-d}$, provided the initial distribution satisfies certain conditions of asymptotic decay. An example, modelling a renewal process and providing a markovian approximation scheme in dynamical system theory, is worked out in detail, illustrating the relationships between convergence behaviour, analytic properties of the generating functions associated to transition probabilities and spectral properties of the Ma…

在流之间随机切换的一类分段确定性马尔可夫过程在有界Lipschitz距离下的指数遍历性

Exponential ergodicity in the bounded-Lipschitz distance for a subclass of piecewise-deterministic Markov processes with random switching between flows

本文研究了一类在流之间随机切换的分段确定性马尔可夫过程，提出了仅依赖流性质和跳跃后位置转移律的可验证条件，以保证其在有界Lipschitz距离下的指数遍历性，并为适用于生物模型的特例建立了简单判据。

Weak KAM methods and ergodic optimal problems for countable Markov shifts

Let $σ:\boldsymbolΣ\to\boldsymbolΣ$ be the left shift acting on $ \boldsymbolΣ $, a one-sided Markov subshift on a countable alphabet. Our intention is to guarantee the existence of $σ$-invariant Borel probabilities that maximize the integral of a given locally Hölder continuous potential $ A : \boldsymbolΣ \to \mathbb R $. Under certain conditions, we are able to show not only that $A$-maximizing probabilities do exist, but also that they are characterized by the fact their support lies actually in a particular Markov subshift on a finite alphabet. To that end, we make use of objects dual to …

Ergodic properties of a class of non-Markovian processes

We study a fairly general class of time-homogeneous stochastic evolutions driven by noises that are not white in time. As a consequence, the resulting processes do not have the Markov property. In this setting, we obtain constructive criteria for the uniqueness of stationary solutions that are very close in spirit to the existing criteria for Markov processes. In the case of discrete time, where the driving noise consists of a stationary sequence of Gaussian random variables, we give optimal conditions on the spectral measure for our criteria to be applicable. In particular, we show that under…

$f$-遍历马尔可夫过程的次指数下界

Subexponential lower bounds for $f$-ergodic Markov processes

本研究提出基于上下鞅条件的准则，通过游程尾下界推导$f$-遍历马尔可夫过程$f$-变差收敛速率的次指数下界，在椭圆扩散等模型中匹配已知上界，提供了类似Lyapunov漂移条件的通用下界方法。

Divergent Predictive States: The Statistical Complexity Dimension of Stationary, Ergodic Hidden Markov Processes

Even simply-defined, finite-state generators produce stochastic processes that require tracking an uncountable infinity of probabilistic features for optimal prediction. For processes generated by hidden Markov chains the consequences are dramatic. Their predictive models are generically infinite-state. And, until recently, one could determine neither their intrinsic randomness nor structural complexity. The prequel, though, introduced methods to accurately calculate the Shannon entropy rate (randomness) and to constructively determine their minimal (though, infinite) set of predictive feature…

A category-theoretic proof of the ergodic decomposition theorem

The ergodic decomposition theorem is a cornerstone result of dynamical systems and ergodic theory. It states that every invariant measure on a dynamical system is a mixture of ergodic ones. Here we formulate and prove the theorem in terms of string diagrams, using the formalism of Markov categories. We recover the usual measure-theoretic statement by instantiating our result in the category of stochastic kernels. Along the way we give a conceptual treatment of several concepts in the theory of deterministic and stochastic dynamical systems. In particular, - ergodic measures appear very natural…