BMAP的轨道分析和Q过程的伴随MAP

本文用轨道分析方法研究批量Markov到达过程(BMAP),有别于研究BMAP常用的矩阵解析方法.通过BMAP的表现(D_k,k=0,1,2,…),得到BMAP的跳跃概率,证明了BMAP的相过程是时间齐次Markov链,求出了相过程的转移概率和密度矩阵.此外,给定一个带有限状态空间的Q过程J,其跳跃点的计数过程记为N,证明了Q过程J的伴随过程X*=(N,J)是一个MAP,求出了该MAP的转移概率和表现(D_0,D_1),它们是通过密度矩阵Q来表述的.     

Some Properties of a Pure Jump Markov Chain after a Cut

Let X={X_t(ω),t≥0} be a pure jump Markov chain with minimal state space I={0,1,2,…} on a probability triple (Ω,F,P), The sample function X(·,ω) is right lower semi-continuous: We denote the transition matrix by p(t)=(p_(ij)(t)) and Q-matrix by Q=(q_(ij))=(p_(ij)~′(0)), i,j∈I, where 0≤sum from f≠i (q_ij)=-q_(ij)=q_i<∞. In this paper let q_i≠0 and. without loss of generality, p(X_n(ω)=i)=1. We define     

族中大偏差的一个不等式

在文献[2]中,F是一有有限期望μ支撑在(-∞,+∞)上的分布函数(d.f.).若其尾分布F=1-F属于D族,那么对任意的γ＞max(μ,0),存在常数C(γ,0),存在常数C(γ)＞0和D(γ)＞0使得C(γ)n(F)(x)≤(Fn*)(x)≤D(γ)n(F)(x),对所有的n≥1和所有的x≥γn成立.本文中我们将其推广成离散情况下精细大偏差的一个不等式,并进一步在连续时间下得到关于部分和S(t)=N(t)∑i=1Xi,t≥0的精细大偏差类似的不等式.     

(D)族中大偏差的一个不等式

在文献[2]中,F是一有有限期望μ支撑在(-∞,+∞)上的分布函数(d.f.).若其尾分布F=1-F属于D族,那么对任意的γ＞max(μ,0),存在常数C(γ,0),存在常数C(γ)＞0和D(γ)＞0使得C(γ)n(F)(x)≤(Fn*)(x)≤D(γ)n(F)(x),对所有的n≥1和所有的x≥γn成立.本文中我们将其推广成离散情况下精细大偏差的一个不等式,并进一步在连续时间下得到关于部分和S(t)=N(t)∑i=1Xi,t≥0的精细大偏差类似的不等式.     

关于时滞系统稳定性的一个问题

1.引言 我们讨论下列时滞系统 (?)(t)=Ax(t)+Bx(t—γ),γ≥0,(1)其中x(t)是时间t的n维向量函数,A和B是n×n的常数矩阵.假定A的特征值都具有负实部. 如果对于任何时滞γ≥0,(1)的零解均为渐近稳定,则称系统(1)为无条件稳定.Hale用二次型加积分项的泛函     

生灭过程构造论

§1.引言设 x(t,ω),t≥0为定义在概率空间(Ω,F,P)上的具可列多个状态的马氏过程,其相空间为 E=(0,1,2,…),转移概率为 P_(ij)(t),i,j∈E,t≥0,它们是一组满足下列条件的实值函数:     

二阶线性矩阵微分系统振动的变分准则

讨论了二阶线性矩阵微分系统(P(t)Y′(t))′+Q(t)Y(t)=0,t≥t0的振动性,其中P(t),Q(t)和Y(t)是n×n实连续矩阵函数, P(t)和Q(t)是对称的,且P(t)是正定的(t≥t0).采用变分方法,得到了该系统振动的向量形式的新准则,并举例进行了验证.     

随机系统的最终稳定性

本文看用李雅普诺夫直接法,建立了随机系统的最稳定性概念及其判断准则。设R=(-∞,+∞),R~+=[0,+∞),R~n为具模|*|的n维线性向量空间,用向量——矩阵记号,考虑随机微分方程组 (?)(t)=f(X,A(t),t) (1)其中A(t)表示随机参变量,向量f的元素f_i关于它的变元连续,(|(?)|0,总存在T=T(ε)∈R~+,对于任意t_0≥T,存在δ=δ(ε,t_0)>0,使得对任何(确定的)初始条件满足     

Markov相依风险模型的等价定理及概率结构

严格定义了Markov相依风险模型.证明了该模型的一个等价定理,使得Markov相依风险模型中的诸过程之间的关系更清晰.获得了Markov相依风险模型的概率结构,构造性地证明了该模型的存在定理.     

指数索赔情形下一类相依两险种风险模型的破产概率

考虑一类稀疏过程下索赔相依的两险种风险模型:U(t)=u+ct-∑i=1N2(t)X_i-∑i=1N2(t)Y_(i),其中{N_1(t),t≥0}、{N_2(t),t≥0}分别表示两个险种的索赔次数,它们按下述方式相关:N_1(t)N_(11)(t)+N_(12)(t),N_2(t)=N_(22)(t)+N'_(12)(t),{N'_(12)(t),t≥0}是{N_(12)(t),t≥0}的一个p-稀疏.考虑下列两种情形:(Ⅰ){N_(11)(t),t≥0}、{N_(12)(t),t≥0}、{N_(22)(t),t≥0}均为Poisson过程;(Ⅱ){N_(11)(t),t≥0}、{N_(22)(t),t≥0}为Poisson过程,{N_(12)(t),t≥0}为Erlang(2)过程.在上述两种情形下,当两险种的单次索赔额均服从指数分布时,通过建立并求解生存概率所满足的微分方程,给出其破产概率的表达式.     

On intersections of independent anisotropic Gaussian random fields

Let XH = {XH(s),s ∈RN1} and X K = {XK(t),t ∈R N2} be two independent anisotropic Gaussian random fields with values in R d with indices H =(H1,...,HN1) ∈(0,1)N1,K =(K1,...,KN2) ∈(0,1) N2,respectively.Existence of intersections of the sample paths of X H and X K is studied.More generally,let E1■RN1,E2■RN2 and FRd be Borel sets.A necessary condition and a sufficient condition for P{(XH(E1)∩XK(E2))∩F≠Ф}>0 in terms of the Bessel-Riesz type capacity and Hausdorff measure of E1×E2×F in the metric space(RN1+N2+d,) are proved,where  is a metric defined in terms of H and K.These results are applicable to solutions of stochastic heat equations driven by space-time Gaussian noise and fractional Brownian sheets.     

Asymptotic Behavior in a Quasilinear Fully Parabolic Chemotaxis System with Indirect Signal Production and Logistic Source

In this paper, we study the asymptotic behavior of solutions to a quasilinear fully parabolic chemotaxis system with indirect signal production and logistic sourceunder homogeneous Neumann boundary conditions in a smooth bounded domain $Ω⊂\mathbb{R}^n$ $(n ≥1)$, where $b ≥0$, $γ ≥1$, $a_i ≥1$, $µ$, $b_i >0$ $(i =1,2)$, $D$, $S∈ C^2([0,∞))$ fulfilling $D(s) ≥ a_0(s+1)^{−α}$, $0 ≤ S(s) ≤ b_0(s+1)^β$ for all $s ≥ 0,$ where $a_0,b_0 > 0$ and $α,β ∈ \mathbb{R}$ are constants. The purpose of this paper is to prove that if $b ≥ 0$ and $µ > 0$ sufficiently large, the globally bounded solution $(u,v,w)$ with nonnegative initial data $(u_0,v_0,w_0)$ satisfies $$\Big\| u(·,t)− \Big(\frac{b}{µ}\Big)^{\frac{1}{γ}}\Big\|_{L^∞(Ω)}+\Big\| v(·,t)−\frac{b_1b_2}{a_1a_2}\Big(\frac{b}{µ}\Big)^{\frac{1}{γ}}\Big\| _{L^∞(Ω)} +\Big\| w(·,t)−\frac{b_2}{a_2}\Big(\frac{b}{µ}\Big)^{\frac{1}{γ}}\Big\| _{L^∞(Ω)}→0$$ as $t→∞$.     

严格伪压缩映象的复合隐格式迭代序列的收敛率估计

设K是实Banach空间E的非空闭凸集,{Ti}iN=1:K→K是N个严格伪压缩映象且公共不动集F=∩Ni=1F(Ti)≠φ,其中F(Ti)={x∈K:Tix=x}.{αn}n∞=1,{βn}n∞=1[0,1]是实序列且满足条件:(i)sum from n=1 to ∞ (αn)(ii)lim(n→∞)αn=lim(n→∞)βn=0(iii)αnβnL2<1,n≥1其中L≥1是{Ti}iN=1的公共Lipschitz常数.对于任意的x0∈K,设{xn}n∞=1是由下列产生的复合隐格式迭代序列:xn=(1-αn)xn-1+αn Tnynyn=(1-βn)xn-1+βnTnxn其中Tn=Tn mod N,则{xn}强收敛到{Ti}iN=1的公共不动点.结果推广和改进了相关文献的结果,且主要定理的证明方法也是不同的.并且进一步给出了序列的收敛率估计.     

由随机过程序列产生的滑动平均过程部分和的弱收敛

本文讨论了由ρ-混合随机过程序列产生的形如Xk(t)=∑j=0∞ajεk-j(t),0≤t≤1,其中{aj;j≥0)为一实数序列,满足∑j=0∞|aj|<∞的滑动平均过程部分和的弱收敛性;同时也讨论了由此滑动平均过程产生的形如Yn(s,t)=1/n~(1/2)∑k=1[n,s]Xk(t),0≤s,t ≤ 1的随机过程的弱收敛性,以及随机足标和SNn(t)=∑k=1NnXk(t)的弱收敛性.     

任意信源与马氏信源比较及小偏差定理

设{X_n,n≥0}是在S={1,2,…N}中取值的可测函数列,P、Q是测度空间上的两个概率测度,其中Q关于{X_n,n≥0}是马氏测度.本文引进了P关于Q的样本散度率距离的概念,并利用这个概念得到了任意信源二元函数一类平均值的小偏差定理,作为推论得到了任意信源熵密度的小偏差定理.最后我们将Shannon-McMillan定理推广到非齐次马氏信源情形.     

伪压缩族公共不动点的复合隐格式迭代逼近问题

设H是一实Hillber空间,K是H之一非空间凸子集,设{Ti}Ni=1是N个Lipschitz伪压缩映象使得F=∩Ni=1F(Ti)≠0,其中F(Ti)={x∈K:Tix=x}并且{αn}n∞=1,{βn}∞n=1[0,1]是满足如下条件的实序列(i)∑∞n=1(1-αn)2= ∞;(ii)limn→∞(1-αn)=0;(iii)∑∞n=1(1-βn)< ∞;(iv)(1-αn)L2<1,n1;(v)αn(1-βn)2 αn[βn L(1-βn)]2<1,其中L1是{Ti}iN=1的公共Lipschitz常数,对于x0∈K,设{xn}n∞=1是由下列定义的复合隐格式迭代xn=αnxn-1 (1-αn)Tnyn,yn=βnxn (1-βn)Tnxn,其中Tn=TnmodN,则(i)limn→∞‖xn-p‖存在,对于所有的p∈F;(ii)limn→∞d(xn,f)存在,其中d(xn,F)=infp∈F‖xn-p‖;(iii)liminfn→∞‖xn-Tnxn‖=0.本文的结果推广并且改进H-K.Xu和R.G.Ori在2001年的结果和Osilike在2004年的结果,并且在这篇文章中,主要的证明方法也不同与H-K.Xu和Osilike的方法.     

The Jump Conditions for Second Order Quasilinear Degenerate Parabolic Equations

ln this paper we consider the model problem for a second order quasilinear degenerate parabolic equation {D_xG(u) = t^{2N-1}D²_xK(u) + t^{N-1}D_x,F(u) \quad for \quad x ∈ R,t > 0 u(x,0) = A \quad for \quad x < 0, u(x,0) = B \quad for \quad x > 0 where A < B, and N > O are given constants; K(u) =^{def} ∫^u_Ak(s)ds, G(u)=^{def} ∫^u_Ag(s)ds, and F(u) =^{def} ∫^u_Af(s)ds are real-valued absolutely continuous functions defined on [A, B] such that K(u) is increasing, G(u) strictly increasing, and \frac{F(B)}{G(B)}G(u) - F(u) nonnegative on [A, B]. We show that the model problem has a unique discontinuous solution u_0 (x, t) when k(s) possesses at least one interval of degeneracy in [A, B] and that on each curve of discontinuity, x = z_j(t) =^{def} s_jt^N, where s_j= const., j=l,2, …, u_0(x, t) must satisfy the following jump conditions, 1°. u_0(z_j(t) - 0, t) = a_j, u_0 (z_j(t) + 0, t) = b_j, and u_0(z_j(t) - 0, t) = [a_j, b_j] where {[a_j, b_j]; j = 1, 2, …} is the collection of all intervals of degeneracy possessed by k (s) in [A, B], that is, k(s) = 0 a. e. on [a_j, b_j], j = 1, 2, …, and k(s) > 0 a. e. in [A, B] \U_j[a_j, b_j], and 2°. (z_j(t)G(u_0(x, t)) + t^{2N-1}D_xK(u_0(x, t)) + t^{N-1}F(u_0(x, t)))|\frac{s=s_j+0}{s=s_j-0} = 0     

星和轮的Mycielski图的[r,s,t]色数

给定非负整数r,s和t,若图G(V,E)有一个映射σ:V∪E→{0,1,…,k-1},k∈N,满足对V中相邻的点v_i,v_j有|σ(v_i)-σ(v_j)|≥r;对E中相邻的边e_i,e_j有|σ(e_i)-σ(e_j)|≥s;对V∪E中相关联的点v_i和边e_j有|σ(v_i)-σ(e_j)|≥t,则称σ为G的一个[r,s,t]-着色.使得图G存在使用了k种颜色的[r,s,t]-着色的最小整数k称为G的[r,s,t]-色数.研究星和轮的Mycielski图的[r,s,t]-着色,并给出其在一定条件下的[r,s,t]-色数.     

Constrained investment–reinsurance optimization with regime switching under variance premium principle

This paper studies optimal investment and reinsurance problems for an insurer under regime-switching models. Two types of risk models are considered, the first being a Markov-modulated diffusion approximation risk model and the second being a Markov-modulated classical risk model. The insurer can invest in a risk-free bond and a risky asset, where the underlying models for investment assets are modulated by a continuous-time, finite-state, observable Markov chain. The insurer can also purchase proportional reinsurance to reduce the exposure to insurance risk. The variance principle is adopted to calculate the reinsurance premium, and Markov-modulated constraints on both investment and reinsurance strategies are considered. Explicit expressions for the optimal strategies and value functions are derived by solving the corresponding regime-switching Hamilton–Jacobi–Bellman equations. Numerical examples for optimal solutions in the Markov-modulated diffusion approximation model are provided to illustrate our results.