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arXiv 1402.0822math.PR
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马尔可夫桥:SDE表示

Markov bridges: SDE representation

Umut Çetin, Albina Danilova

摘要:

设$X$是一个在$\mathbf{E}$中取值的马尔可夫过程,具有连续路径和转移函数$(P_{s,t})$ .给定$(\mathbf{E}, \mathscr{E})$上的测量$μ$,对于$T^* <\infty$,从$(s,\varepsilon_x)$开始并在$(T^*,μ)$结束的马尔可夫桥具有原始过程的规律,该规律在时间$s$处从$x$开始并被调节为在时间$T^*$处具有规律$μ$。我们将考虑两种类型的条件作用:a)当$μ$相对于$P_{s,t}(x,\cdot)$是绝对连续的时,{ \em弱条件作用}; b)当$μ=\varepsilon_z$对于某些$z \in \mathbf{E}$是绝对连续的时,{ \em强条件作用}。本文的主要结果是表示的马尔可夫桥作为一个解决方案的随机微分方程驱动的布朗运动在扩散设置。在较弱的条件下,证明了该扩散过程弱解和强解的存在唯一性。

英文摘要:

Let $X$ be a Markov process taking values in $\mathbf{E}$ with continuous paths and transition function $(P_{s,t})$. Given a measure $μ$ on $(\mathbf{E}, \mathscr{E})$, a Markov bridge starting at $(s,\varepsilon_x)$ and ending at $(T^*,μ)$ for $T^* <\infty$ has the law of the original process starting at $x$ at time $s$ and conditioned to have law $μ$ at time $T^*$. We will consider two types of conditioning: a) {\em weak conditioning} when $μ$ is absolutely continuous with respect to $P_{s,t}(x,\cdot)$ and b) {\em strong conditioning} when $μ=\varepsilon_z$ for some $z \in \mathbf{E}$. The main result of this paper is the representation of a Markov bridge as a solution to a stochastic differential equation (SDE) driven by a Brownian motion in a diffusion setting. Under mild conditions on the transition density of the underlying diffusion process we establish the existence and uniqueness of weak and strong solutions of this SDE.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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