基于动态规划的高阶隐马氏模型推广的Viterbi算法

首先通过Hadar等价变换方法将高阶隐马氏模型转换为与之等价的一阶向量值隐马氏模型,然后利用动态规划原理建立了一阶向量值隐马氏模型的Viterbi算法,最后通过高阶隐马氏模型和一阶向量值隐马氏模型之间的等价关系建立了高阶隐马氏模型基于动态规划推广的Viterbi算法．研究结果在一定程度上推广了几乎所有隐马氏模型文献中所涉及到的解码问题的Viterbi算法,从而进一步丰富和发展了高阶隐马氏模型的算法理论．     

一类带随机延滞的时间序列模型的遍历性

本利用马氏化方法和一般状态空间马氏链的基本理论研究了一类带随机延滞的时间序列模型的遍历性，得到了该模型伴随几何遍历的一个判别准则．     

高阶隐马氏模型研究进展

首先分析了高阶隐马氏模型的研究动机和背景,并给出了高阶隐马氏模型的一般结构和定义,然后总结了高阶隐马氏模型的一般研究方法,最后展望了高阶隐马氏模型进一步的研究方向.     

隐Markov模型的强马氏性

引入隐Markov模型强马氏性的概念,并进一步研究了隐Markov模型在强马氏性方面的一些性质.     

可列非齐次马氏链二元泛函的强大数定律

本文研究可列非齐次马氏链二元泛函的强大数定律，并利用这个结果研究可列非齐次马氏链Shannon－McMillan定理．     

一类点边同时变化的增长网络的度分布

本文研究一个节点和连边能同时发生变化的网络模型,把模型中的节点度的演化过程看成一族马氏链。从模型的演化机制中得到该马氏链的状态转移概率,用马氏链的方法证明了该模型的稳态度分布是存在的,并得到了度分布的精确表达式。从而说明了该网络是标度指数为3的无标度网络。     

离散型冲击折扣半马氏决策过程

本文讨论离散型冲击折扣半马氏决策过程，在建立模型后，我们将它化成了一个等价的离散时间马氏决策过程．     

经2状态马氏修正的Poisson过程的极大似然估计

近年来。隐马氏模型成为研究相依随机变量的一个十分有用的工具。应用过程中的一个很重要的问题是如何对隐马氏模型的参数进行估计。一般使用的方法是将连续时间隐马氏模型的问题转化为离散时间隐马氏模型的问题来讨论。本文用此方法讨论一类连续时间隐马氏模型——状态个数为2的经马氏链修正的Poisson过程的极大似然估计及其算法。此类模型被广泛用来对复杂通信网络的通信流进行建模。     

马尔可夫模型的市场预测方法及其应用

本文简述了马尔可夫模型的基本原理，介绍了利用马氏过程的基本特征．即其平稳性和无后效性．对随机现象进行统计预测的方法。本文选取市场为研究对象，根据产品的市场占有率的随机变化过程，建立起市场占有率的马尔可夫预测模型，并进行了实例分析。在模型的实际应用时，要注意系统状态转移的平稳性和进行系统状态划分的有效性。     

马氏调制散粒噪声模型在保险中的应用

在常利率环境下,研究了一个含相关业务类的复合Cox风险模型,其保险业务间的相关结构是由共同跳结构来描述的.假设两类保险业务的理赔来到强度过程服从一个多维的马氏调控散粒噪声过程.利用鞅方法,给出了总折现理赔量和马氏调控散粒噪声强度过程的联合拉普拉斯变换,并利用拉普拉斯变换给出了总折现理赔量的期望和方差等特征量.     

Theoretical analysis and numerical simulation of Parrondo’s paradox game in space

A multi-agent spatial Parrondo game model is designed according to the cooperative Parrondo’s paradox proposed by Toral. The model is composed of game A and game B. Game A is a zero-sum game between individuals, reflecting competitive interaction between an individual and its neighbors. The winning or losing probability of one individual in game B depends on its neighbors’ winning or losing states, reflecting the dependence that individuals has on microhabitat and the overall constraints that the microhabitat has on individuals. By using the analytical approach based on discrete-time Markov chain, we analyze game A, game B and the random combination of game A+B, and obtain corresponding stationary distribution probability and mathematical expectations. We have established conditions of the weak and strong forms of the Parrondo effect, and compared the computer simulation results with the analytical results so as to verify their validity. The analytical results reflect that competition results in the ratchet effect of game B, which generates Parrondo’s Paradox that the combination of the losing games can produce a winning result.     

大工程决策中公众意愿与地方政府行为偏好研究

在公共事务决策中公众参与正影响着地方政府决策。基于博弈视角在地方政府与公众的策略互动中,博弈收敛速度的提高能够降低双方的成本从而提高社会效率。建立基于大工程议案决策的地方政府与公众动态博弈过程,应用马尔可夫决策研究博弈收敛的条件并分析了基于折扣准则下地方政府采取妥协策略时的最优停止条件。讨论了地方政府对不稳定的容忍度、地方政府选择妥协的概率与博弈收敛速度三者之间的关系。研究表明大工程预期经济收益指标与地方政府对地方不稳定的容忍度呈正向关系,地方政府对地方不稳定的容忍度与双方关于大工程议案决策博弈的收敛速度呈反向关系,且公众支持度对地方容忍度的影响依概率分布变化。     

中国证券市场知情交易概率的动态马尔科夫状态转移模型研究

对指令驱动市场知情交易的研究是近年来的热点问题。常用的EKOP模型存在一些缺陷,本文放宽了EKOP模型关于日内信息均匀释放以及交易者行为独立性的假设,用动态的马尔科夫状态转移模型对该模型进行了改进,并检验了改进后的知情交易概率模型在中国证券市场的适用性。通过模拟数据以及对中国证券市场交易数据的实证研究发现动态的马尔科夫状态转移模型克服了EKOP模型受买卖方数据影响而产生的系统偏误,估计的知情交易概率更符合事后检验。     

基于全面预算管理的预算编制与审批的动态博弈

在全面预算管理体系下,重点探究预算申请与审批过程中的博弈关系,通过建立一个含不确定性因素的完全但不完美信息的动态博弈模型,找到博弈参与方决策的主要决定因素,研究怎样才能降低申请人虚报预算和审批人渎职宽审的概率,从而为这一问题的解决思路提供理论依据.     

Mover‐stayer model with covariate effects on stayer's probability and mover's transitions

A discrete time Markov chain assumes that the population is homogeneous, each individual in the population evolves according to the same transition matrix. In contrast, a discrete mover‐stayer (MS) model postulates a simple form of population heterogeneity; in each initial state, there is a proportion of individuals who never leave this state (stayers) and the complementary proportion of individuals who evolve according to a Markov chain (movers). The MS model was extended by specifying the stayer's probability to be a logistic function of an individual's covariates but leaving the same transition matrix for all movers. We further extend the MS model by allowing each mover to have her/his covariates dependent transition matrix. The model for a mover's transition matrix is related to the extant Markov chains mixture model with mixing on the speed of movement of Markov chains. The proposed model is estimated using the expectation‐maximization algorithm and illustrated with a large data set on car loans and the simulation.     

Generalizations of bold play in red and black

The strategy of bold play in the game of red and black leads to a number of interesting mathematical properties: the player's fortune follows a deterministic map, before the transition that ends the game; the bold strategy can be “re-scaled” to produce new strategies with the same win probability; the win probability is a continuous function of the initial fortune, and in the fair case, equals the initial fortune. We consider several Markov chains in more general settings and study the extent to which the properties are preserved. In particular, we study two “k-player” models.     

不完全信息下基于价格信号的投资博奕模型

在金融市场中,投资者的预期是决定市场走向的重要因素,而机构投资者的资源禀赋与其市场操纵行为的获利之间往往呈现正相关关系.通过描述机构和散户基于价格信号传递的对策过程,建立了一个博弈模型,模型演绎结果在理论上证实了上述经济现实.     

A two-person zero-sum Markov game with a stopped set

We consider a two-person zero-sum Markov game with continuous time up to the time that the game process goes into a fixed subset of a countable state space, this subset is called a stopped set of the game. We show that such a game with a discount factor has optimal value function and both players will have their optimal stationary strategies. The same result is proved for the case of a nondiscounted Markov game under some additional conditions, that is a reward rate function is nonnegative and the first time τ (entrance time) of the game process going to the stopped set is finite with probability one (i.e., p(τ < ∞) = 1). It is remarkable that in the case of a nondiscounted Markov game, if the expectation of the entrance time is bounded, and the reward rate function need not be nonnegative, then the same result holds.     

Basic problems solving for two-dimensional discrete 3 × 4 order hidden markov model

A novel model is proposed to overcome the shortages of the classical hypothesis of the two-dimensional discrete hidden Markov model. In the proposed model, the state transition probability depends on not only immediate horizontal and vertical states but also on immediate diagonal state, and the observation symbol probability depends on not only current state but also on immediate horizontal, vertical and diagonal states. This paper defines the structure of the model, and studies the three basic problems of the model, including probability calculation, path backtracking and parameters estimation. By exploiting the idea that the sequences of states on rows or columns of the model can be seen as states of a one-dimensional discrete 1 × 2 order hidden Markov model, several algorithms solving the three questions are theoretically derived. Simulation results further demonstrate the performance of the algorithms. Compared with the two-dimensional discrete hidden Markov model, there are more statistical characteristics in the structure of the proposed model, therefore the proposed model theoretically can more accurately describe some practical problems.     

Stability of the LCD model

The growing network model with loops and multiple edges proposed by Bollobás et al. (Random Structures and Algorithms 18(2001)) is restudied from another perspective. Based on the first-passage probability of Markov chains, we prove that the degree distribution of the LCD model is power-law with degree exponent 3 as the network size grows to infinity.