水库储水建模的马尔可夫骨架过程方法

在本中，我们应用马尔可夫骨架过程的理论，建立了水库储水模型，并且用向后方程刻画了水库储水过程的一维分布,这一模型是随机环境流体模型的推广，在金融管理以及网络技术等领域中都有重要应用．     

随机脉冲干扰对B—CKdV方程扭状孤波解的影响

本文利用侯振挺等^[1]提出的马尔可夫骨架过程理论求出了在随机随冲干扰下,B－CKdV方程ut auux bu^2ux ruxx uxxx=0的行波解及其一维分布,并研究了行波解的渐近性质。     

马尔可夫骨架PERT网络的最长路径

本文利用马尔可夫骨架过程理论研究PERT网络模型，其中网络各弧线的长度是相互独立的随机变量。文中构造了一个马尔可夫骨架过程，利用其向后方程求解随机网络最长路径长度的分布函数。     

半马尔可夫过程的极限分布及其推广

本文运用马尔可夫骨架过程的极限理论研究齐次可列半马尔可夫过程,得到其极限分布.当更新间隔的分布不是格子分布时,本文的结果和邓永录等[1]中的结果一致,但采用的方法不同,本文采用的是马尔可夫骨架过程的理论方法,而[1]中采用的是交替更新过程的方法;而且关于更新间隔服从格子分布的情形,[1]中没有研究,而本文给出了结果.最...     

具有马尔可夫骨架的随机过程(英文)

在本文中，我们引入了具有马尔科夫骨架的随机过程的概念．具体讨论了一类重要的具有马氏骨架的随机过程─—（H，Q）─过程，得到了计算这种过程概率分布的向后和向前方程，计算了过程的一维分布以及给出了这种过程正则的充分必要条件．并且本文绘出了文献[3]中主要结果的证明．     

商店出售易腐烂物品所得收益的马尔可夫骨架过程建模

本文中,我们应用马尔可夫骨架过程的理论建立了商店出售易腐烂物品所得盈利的数学模型,并且用向后方程刻画了盈利额的一维分布.     

马尔可夫骨架过程的极限分布

本文运用基本更新定理和Smith关键更新定理等理论和方法,对马尔可夫骨架过程的极限分布进行深入研究,得到主要结果如下:去掉了原有结果中要求的绝对连续的条件,给出了马尔可夫骨架过程极限分布存在的充分条件;得到了马尔可夫骨架过程极限分布的具体公式,并证明了该极限分布为概率分布.     

Markov骨架过程积分型泛函的分布和矩及其应用举例

侯振挺等^[1]引入了一类具有广泛应用前景的随机过程－Markov骨架过程,本文研究这类过程积分型泛函的分布和矩及其计算问题,作为应用,我们得到了Doob过程,生灰2过程积分型泛函的分布和矩的公式,尤其对于生灭过程,利用本文的方法也得到了[4]中定理1－3的结果。     

SMAP／INDI／1单重休假随机服务系统的MSP方法

本文借助于马尔可夫骨架过程(MSP)方法研究了SMAP/INID/1单重休假随机服务系统的队长及等待时间等指标的瞬时分布.     

一类马尔可夫骨架过程的强大数定律和中心极限定理

基于马尔可夫骨架过程极限分布的已有研究结果,本文运用波莱尔-康特立引理、更新理论、科尔莫哥洛夫的强大数定律以及独立同分布情形的中心极限定理等重要理论,分别给出了一类马尔可夫骨架过程对应的累积过程满足强大数定律和中心极限定理的充分条件.     

GI(1)+GI(2)+…+GI(N)/M/1排队模型

文献[1]引入了一类具有广泛应用前景的随机过程--Markov骨架过程.本文借助这类随机过程的方法研究了GI(1)+GI(2)+…+GI(N)/M/1排队模型,求出了此模型到达过程、等待时间及队长的概率分布.     

GI／G／1单重休假随机服务系统的马氏骨架方法

文献[1]引入一类具有广泛应用前景的随机过程-Markov骨架过程。借助Markov骨架过程的方法研究GI/G/1单重休假服务系统队长，及t时刻到达顾客等待时间的瞬时概率分布。     

多重休假GI／G／1排队系统的非统计平衡理论

文献[1]引入了一类具有广泛应用前景的随机过程-Markov骨架过程，文献[2]研究了GI/G/1排队系统，本文对其进行了拓展，研究了多重休假GI/G/1排队模型。求出了此模型的到达过程，等待时间及队长的概率分布。     

The Lévy Laplacian and stable processes

In this paper we give stochastic processes generated by powers of the Lévy Laplacian acting on a space of generalized white noise distributions using stable processes.     

Retrieval of Black-Scholes and generalized Erlang models by perturbed observations at a fixed time

S-stable laws on the real line (more generally on Hilbert spaces), associated with some non-linear transformations (so-called “shrinking operations”), were introduced in [Jurek, Z.J., 1977. Limit distributions for truncated random variables. In: Proc. 2nd Vilnius Conference on Probability and Statistics, June 28-July 3, 1977. In: Abstracts of Communications, vol. 3, pp. 95-96; Jurek, Z.J., 1979. Properties of s-stable distribution functions. Bull. Acad. Polon. Sci. Sér. Math. XXVII (1), 135-141; Jurek, Z.J., 1981. Limit distributions for sums of shrunken random variables. Dissertationes Math. vol. CLXXXV]. In [Jurek, Z.J., Neuenschwander, D., 1999. S-stable laws in insurance and finance and generalization to nilpotent Lie groups. J. Theoret. Probab. 12 (4), 1089-1107], the authors interpreted s-stable motions on the real line as limits of total amount of claims processes (up to a deterministic premium) of a portfolio of excess-of-loss reinsurance contracts and showed that they led to Erlang’s model or to Brownian motion. In [Neuenschwander, D., 2000b. On option pricing in models driven by iterated integrals of Brownian motion. In: Mitt. SAV 2000, Heft 1, pp. 35-39], we considered stochastic integrals whose integrand and integrator are both independent Brownian motions, thus modelling a stochastic volatility; as a result we got an analogue of the Black-Scholes formula in this model, confirming a result of Hull and White [Hull, J., White, A., 1987. The pricing of options on assets with stochastic volatility. J. Finance XLII (2), 281-300]. In the present paper, we will look at a common generalization of these processes, namely s-stable motions on the real line perturbed by a stochastic integral whose integrand and integrator are both (not necessarily independent) s-stable motions. The main result will be that if we can observe the distribution of such so-perturbed s-stable motions (together with the values of the perturbing processes) at time t=1, then we can identify the whole model (including the perturbation) among all models with Lévy processes perturbed by an iterated stochastic integral of two Lévy processes (in the gaussian case) resp. among all models with a compound Poisson process with drift perturbed by an iterated stochastic integral of two compound Poisson processes (in the completely non-gaussian case if the perturbing processes have no drift) without knowing anything about the history or about its distribution during 0≤t<1. This applies, e.g., to a situation where several assets obey the same model and one can estimate the distribution at time one by looking at the values of all these assets at time t=1.Interestingly enough, it will be convenient to treat the whole matter in the algebraic framework of the so-called Heisenberg group. This is a concept coming in fact from quantum mechanics and is in a certain sense the simplest non-commutative Lie group.     

Statistical causality and adapted distribution

In the paper D. Hoover, J. Keisler: Adapted probability distributions, Trans. Amer. Math. Soc. 286 (1984), 159–201 the notion of adapted distribution of two stochastic processes was introduced, which in a way represents the notion of equivalence of those processes. This very important property is hard to prove directly, so we continue the work of Keisler and Hoover in finding sufficient conditions for two stochastic processes to have the same adapted distribution. For this purpose we use the concept of causality between stochastic processes, which is based on Granger’s definition of causality. Also, we provide applications of our results to solutions of some stochastic differential equations.     

EPSTA: The coincidence of time-stationary and customer-stationary distributions

In the first part of this paper we present an overview of relationships between time- and customer-stationary distributions of queueing processes. These have been proved by using the properties of random marked point processes, stochastic processes with embedded point processes, Palm distributions and an intensity conservation principle. In the second part a necessary and sufficient condition is established for the coincidence of the two types of stationary distributions, using conditional intensities. We also formulate the property of EPSTA that includes PASTA and ASTA as particular cases. A further result concerns the conditional EPSTA property. Applications to particular queueing systems are considered.     

Representations of general stochastic processes

In this paper we carry on our study [4] of the algebraic representations of general stochastic processes. We give methods for constructing the algebraic representation of a stochastic process from the distribution of the process at a fixed finite number of times, we develope some techniques of integration, and we introduce the notion of a fibre bundle representation of a stochastic process. We then use this fibre bundle representation to study existence, methods of computation and the geometry of Markov process representations of the general stochastic process; thus extending [4] where existence was only discussed for discrete time or simple stochastic processes.