二叉树上分枝马氏链的强大数定理

首先给出了在可列状态空间取值的二叉树上分枝马氏链定义的离散形式,然后建立了二叉树上分枝马氏链的若干强极限定理,最后研究了二叉树上有限状态分枝马氏链的强大数定理．     

A new infinite product representation for real numbers

We introduce a new algorithm that leads to a representation for any real number greater than one as an infinite product of rational numbers. Just as we can regard the Cantor product as being a product analogue of the series of Sylvester, this new product is analogous to the classical Engel representation for real numbers. The growth conditions satisfied by the digits in the product are likewise shown to correspond to those required for the Engel series. The representation for certain types of rational numbers via this algorithm is also considered.     

Strong law of large numbers for Markov chains indexed by an infinite tree with uniformly bounded degree

In this paper,we study the strong law of large numbers and Shannon-McMillan (S-M) theorem for Markov chains indexed by an infinite tree with uniformly bounded degree.The results generalize the analogous results on a homogeneous tree.     

The effect of memory on functional large deviations of infinite moving average processes

The large deviations of an infinite moving average process with exponentially light tails are very similar to those of an i.i.d. sequence as long as the coefficients decay fast enough. If they do not, the large deviations change dramatically. We study this phenomenon in the context of functional large, moderate and huge deviation principles.     

A weak law of large numbers for maxima

A weak law of large numbers related to the classical Gnedenko results for maxima (see Gnedenko, Ann Math 44:423–453, 1943) is established.     

A general law of large numbers for array ofL-R fuzzy numbers

We study a general law of large numbers for array of mutuallyT related fuzzy numbers whereT is an Archimedeant-norm and generalize earlier results of Fullér(1992), Triesch(1993) and Hong(1996).     

A strong law of large numbers for non-additive probabilities

In this paper, with the notion of independence for random variables under upper expectations, we derive a strong law of large numbers for non-additive probabilities. This result is a natural extension of the classical Kolmogorov’s strong law of large numbers to the case where the probability is no longer additive. As an application of our result, we give most frequent interpretation for Bernoulli-type experiments with ambiguity.     

A bivariate strong law of large numbers

Probability Theory and Related Fields -     

A generalized strong law of large numbers

Strong laws of large numbers have been stated in the literature for measurable functions taking on values on different spaces. In this paper, a strong law of large numbers which generalizes some previous ones (like those for real-valued random variables and compact random sets) is established. This law is an example of a strong law of large numbers for Borel measurable nonseparably valued elements of a metric space. Received: 24 February 1998 / Revised version: 3 January 1999     

A law of large numbers for moderately interacting diffusion processes

Summary We consider two special models of interacting diffusion processes, and derive in the limit, as the number of different processes tends to infinity and the interaction is rescaled in a suitable (“moderate”) way, a law of large numbers for the empirical processes. As limit dynamics we obtain certain nonlinear diffusion equations. This work has been supported by the Deutsche Forschungsgemeinschaft.     

A strong law of large numbers for orthogonal random variables

A generalization of one theorem of K. Tandori is proved. A sufficient condition is derived for application of a strong law of large numbers to a sequence of orthogonal random variables, expressed in terms of the growth of sums of second moments of these variables.     

A strong law of large numbers for fuzzy random variables

A limit of a sequence of fuzzy numbers is defined and its some properties are shown. Based on these concept and properties, an independent sequence of fuzzy random variables is considered and a strong law of large numbers for fuzzy random variables is shown.     

The strong law of large numbers for a stationary sequence

General results on the applicability of the strong law of large numbers to a sequence of dependent random variables, as formulated in terms of estimates for the moments of sums of such variables, are applied to give new conditions of the applicability of this law to (in a wide sense) a stationary sequence of random variables.     

Strong law of large numbers for weighted sums

The law of large numbers for weighted sums of independent identically distributed random variables due to Chow and Lai is generalized to nonidentically distributed or pairwise independent summands. Supported by the Russian Foundation for Fundamental Research (grant No. 96-01-01920). Proceedings of the Seminar on Stability Problems for Stochastic, Models, Moscow, Russia, 1996, Part. II.     

Weak law of large numbers for linear processes

We establish sufficient conditions for the Marcinkiewicz–Zygmund type weak law of large numbers for a linear process \({\{X_k:k\in\mathbb Z\}}\) defined by \({X_k=\sum_{j=0}^\infty\psi_j\varepsilon_{k-j}}\) for \({k\in\mathbb Z}\), where \({\{\psi_j:j\in\mathbb Z\}\subset\mathbb R}\) and \({\{\varepsilon_k:k\in\mathbb Z\}}\) are independent and identically distributed random variables such that \({{x^p\Pr\{|\varepsilon_0| > x\}\to 0}}\) as \({{x\to \infty}}\) with \({1 < p < 2}\) and \({E \varepsilon_0=0}\). We use an abstract norming sequence that does not grow faster than \({n^{1/p}}\) if \({\sum|\psi_j| < \infty}\). If \({\sum|\psi_j|=\infty}\), the abstract norming sequence might grow faster than \({n^{1/p}}\) as we illustrate with an example. Also, we investigate the rate of convergence in the Marcinkiewicz–Zygmund type weak law of large numbers for the linear process.