全概率公式的推广及其应用

全概率公式及其思想在概率统计与随机过程中具有重要作用.给出了公式在条件概率下的推广形式与具体应用.同时,得到了独立性条件下的特殊形式.     

具有马氏调制费率的复合Poisson风险模型的破产概率

对于给定的初始状态和初始分布 ,本文分别给出了条件破产概率 Ψi(u)和最终破产概率 Ψ(u)所满足的积分方程 ,并给出了零初始资产时破产概率 Ψ(0 )的明确表达式 .     

基于统计自然语言处理的央行货币政策研究

运用自然语言处理方法来分析货币政策,对数据采用绝对概率和条件概率方法分别分析.得出如下结论:第一,在我国近十几年的货币政策实施过程中,央行对于"通货膨胀"的关注度明显高于"通货紧缩".第二,通过统计有关通胀类词的出现个数,可以大致了解每年通货膨胀严重程度.第三,通过计算条件概率,可以更好的解释该给条件词十几年的大致走势.第四,通过不同时期的比较分析,在绝对概率下可以更好的看出用词的变迁,而在条件概率下,可以更好的研究给定词在不同时期所表现出来的不同特征.     

条件概率及其推广

本文在概率空间及事件域意义下解释通常意义下的条件概率定义，并借此形式推广了条件概率定义。     

A conditional limit construction of the normal probability density

Summary It is shown that a normal probability density can be characterized as a limit of conditional probability densities of i.i.d. uniform random variables.     

计算数学期望和概率的条件化方法

借助于条件数学期望和随机事件A的示性函数IA,通过对随机变量的适当"条件化"处理,应用全期望公式和推广的全概率公式,讨论了计算数学期望和概率的条件化方法.     

On tail behavior of nonlinear autoregressive functional conditional heteroscedastic model with heavy-tailed innovations

We study the tail probability of the stationary distribution of nonparametric non- linear autoregressive functional conditional heteroscedastic (NARFCH) model with heavy- tailed innovations.Our result shows that the tail of the stationary marginal distribution of an NARFCH series is heavily dependent on its conditional variance.When the innovations are heavy-tailed,the tail of the stationary marginal distribution of the series will become heavier or thinner than that of its innovations.We give some specific formulas to show how the increment or decrement of tail heaviness depends on the assumption on the con- ditional variance function.Some examples are given.     

一类推广的带干扰风险模型的条件破产概率

本文首先利用随机时刻变换推广了一类带干扰的风险模型,然后讨论这类风险模型的条件破产概率.研究表明条件破产概率相对于无条件破产概率能提供更多有用信息,这对保险公司及时调整投资和管理策略是很有帮助的.     

Conditional Probability on the Kopka＇s D-posets

K?pka’s D-poset is a very important notion in quantum structures. In this paper the conditional probability on the K?pka’s D-posets is studied. The notion of conditional probability is introduced and the basic properties of conditional probability are proved.     

一类具有马氏调制费率的风险模型的破产概率

对于给定的初始状态，本给出了条件破产所满足的积分方程。并推出了在具有平稳初始分布时破产概率的递归不等式和零初始资产时破产概率的一个简洁估计式。     

索赔到达过程为马氏跳过程的一类风险模型

论将索赔到达点过程由Poisson点过程推广为由马氏链的跳跃点形成的点过程，保费收取由净收入随机确定，我们得到破产概率ψ（u)及条件破产概率φi(u)满足的积分方程．     

树上路径过程的随机路径条件概率的极限性质

本文研究了树上路径过程的极限性质.利用构造鞅的方法得到了树上路径过程的条件概率调和平均的极限性质.所得结果推广了树上非齐次马氏链随机转移概率和任意随机变量序列随机条件概率的调和平均极限性质.     

A note on the conditional probability of <Emphasis Type="Italic">IF</Emphasis>-events

We formulate a version of the conditional probability of an IF-event considering the Łukasiewicz operations with IF-sets. Also some properties of conditional probability are proved. The results are strengthenings of preceding ones published in [LENDELOVá, K.: Conditional IF-probability. In: Soft Methods for Integrated Uncertainty Modelling. Proceedings of the 2006 InternationalWorkshop on Soft Methods in Probability and Statistics (SMPS 2006), Bristol, UK, September 5–7, 2006. Adv. Soft Comput., Springer-Verlag, Berlin, 2006, pp. 275–283]. The paper was supported by Grant VEGA 1/0539/08.     

A note on a journal selection problem

This note explores the convergence properties of certain sequences of conditional probabilities arising in a journal selection problem, where the probabilities of interest are decreasing in either a deterministic or stochastic fashion. We prove the convergence to a nonextreme value of the probability of an eventual event for any choice of problem parameters within the open unit interval. Computational results illustrate the convergence properties.     

负二项分布类的条件概率封闭性

在研究只允许部分服务台进入休假状态的多服务台M／M／c排队系统时，我们发现了条件Erlang分布的一些有趣的性质，进一步研究我们发现相对应离散随机状态的负二项分布也具有很好的性质（概率封闭性．本文证明了一类负二项分布的概率封闭性．它们对导出复杂排队系统中离散状态下顾客等待时问分布及保险公司中破产概率上界的计算起着重要作用．     

Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models

This paper deals with a scalar response conditioned by a functional random variable. The main goal is to estimate nonparametrically some characteristics of this conditional distribution. Kernel type estimators for the conditional cumulative distribution function and the successive derivatives of the conditional density are introduced. Asymptotic properties are stated for each of these estimates, and they are applied to the estimations of the conditional mode and conditional quantiles. Our asymptotic results highlightes the importance of the concentration properties on small balls of the probability measure of the underlying functional variable. So, a special section is devoted to show how our results behave in several situations when the functional variable is a continuous time process, with special attention to diffusion processes and Gaussian processes. Even if the main purpose of our paper is theoretical, an application to some chemiometrical data set coming from food industry is presented in a short final section. This example illustrates the easy implementation of our method as well as its good behaviour for finite sample sizes.     

Simulation metamodeling with dynamic Bayesian networks

This paper presents a novel approach to simulation metamodeling using dynamic Bayesian networks (DBNs) in the context of discrete event simulation. A DBN is a probabilistic model that represents the joint distribution of a sequence of random variables and enables the efficient calculation of their marginal and conditional distributions. In this paper, the construction of a DBN based on simulation data and its utilization in simulation analyses are presented. The DBN metamodel allows the study of the time evolution of simulation by tracking the probability distribution of the simulation state over the duration of the simulation. This feature is unprecedented among existing simulation metamodels. The DBN metamodel also enables effective what-if analysis which reveals the conditional evolution of the simulation. In such an analysis, the simulation state at a given time is fixed and the probability distributions representing the state at other time instants are updated. Simulation parameters can be included in the DBN metamodel as external random variables. Then, the DBN offers a way to study the effects of parameter values and their uncertainty on the evolution of the simulation. The accuracy of the analyses allowed by DBNs is studied by constructing appropriate confidence intervals. These analyses could be conducted based on raw simulation data but the use of DBNs reduces the duration of repetitive analyses and is expedited by available Bayesian network software. The construction and analysis capabilities of DBN metamodels are illustrated with two example simulation studies.     

残缺数据下动态随机算法及应用

针对指标数据残缺的动态评价问题,提出了一种基于残缺数据的动态随机算法。首先依据时间维度上的分布情况将残缺值分为两类:离散型与连续型,并提出了对应的补足方法;然后在数据补足完整的基础上,利用随机模拟技术,计算优胜度矩阵,并推导出被评价对象之间的可能性排序。该算法避免了评价对象之间排序的绝对性,在对实际问题的解释方面具有较大弹性。最后,通过一个算例对该算法进行详细说明。     

常利率下的Cox模型的破产概率

讨论了常利率下的Cox模型的破产概率 ,分别得到了条件破产概率和最终破产概率所满足的积分方程 .     

Correction of incoherent conditional probability assessments

In this paper we deep in the formal properties of an already stated discrepancy measure between a conditional assessment and the class of unconditional probability distributions compatible with the assessment domain.