1) Backward doubly stochastic differential equations with stopping time
带停时的倒向重随机微分方程
3) backward stochastic differential equations with jumps
带跳倒向随机微分方程
1.
A stability theorem of the solutions is derived to the following backward stochastic differential equations with jumps y~ε_t=ξ~ε+∫~T_tf~ε(s,y~ε_s,z~ε_s,v~ε_s)ds-∫~T_tz~ε_sdw_s-∫~T_t∫_Uv~ε_s(z)(ds,dz),ε≥0,t∈ under non-Lipschitz condition and the main tool is a corollary of the Bihari inequality.
证明了带跳倒向随机微分方程列ytε=ξε+∫tTfε(s,ysε,zsε,vsε)ds-∫tTzsεdws-∫∫tTUvεs(z)N(ds,dz),ε≥0,t∈[0,T]在非Lipschitz条件下其解的稳定性;使用的主要工具是Bihari不等式的一个推论。
4) backward doubly stochastic differential equation
倒向重随机微分方程
1.
The comparison theorem of backward doubly stochastic differential equations with Poisson process(BDSDEP) can be obtained under Lipschitz condition by means of Gronwall inequality,Young inequality,and It formula,which means the solution increases with the coefficient and the terminal value of BDSDEP.
在Lipschitz条件下,利用Gronwall不等式、Young不等式和Ito^公式等,得到了带跳的倒向重随机微分方程解的比较定理,说明了带跳的倒向重随机微分方程的系数和终端值越大,其解越大。
5) backward doubly stochastic differential equations
倒向重随机微分方程
1.
Backward Doubly Stochastic Differential Equations under Non-Lipschitzian Coefficient;
非Lipschitz条件下的倒向重随机微分方程
2.
We establish a new connection between solutions of backward doubly stochastic differential equations(BDSDEs)on infinite horizon and the station-ary solutions of the SPDEs.
我们首次将无穷区间上的倒向重随机微分方程(BDSDE)的解与SPDE的平稳解联系起来。
3.
The comparison theorem of backward doubly stochastic differential equations(BDSDE) with jump can be obtained under non-Lipschitz condition by means of Gronwall inequality and Ito\'s formula.
研究了一类带跳的倒向重随机微分方程在非Lipschitz条件下的比较定理。
6) backward doubly stochastic differential equations
双重倒向随机微分方程
1.
Comparision theorem for multi-dimensional backward doubly stochastic differential equations;
多维双重倒向随机微分方程比较定理
补充资料：停时
停时
stopping time;
停时[咖lpl啾山祀;oc拙10训BpeM”」[fI、注】设抓，作T，是可测空间(measulable sPilce)(。，劝上的非减子a代数族，此处T是【0，田」中的一区问或{0，1，…}日{刃}的一子集，则停时(‘一J这一子代数族相关的)是一个映射(随机变量(，:川由mva‘able))::。，T日{的}，使得 {T(‘。)簇弓〔心，对一l)Jt任T成立.这一随机变量也称为可选随机变量(oPtiontll rdndom vdriable).这一条件解释为时间值随机变量t不具有未来的知识，因为a代数式概括了“直到时刻t的随机事件”.许多停时由“在该时刻给定的事件被首次观察到”产生.例如，随机过程X(t)首次进人(firstti服of entry)集合A(击中11、」(Ilitti一19 time)).在俄文文献中术语Map劝。时tMarkovmon祀11t，Markovtime)常用来表示停时.有时也见到术语非预料时(11on一anticipating tin犯).停时在最优停止问题(optiTnal stopping problenl)中自然会出现.例如，见【A4].
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参考词条