We establish a recursive representation that fully decouples jumps from a large class of multivariate inhomogeneous stochastic differential equations with jumps of general time-state dependent unbounded intensity, not of L\'evy-driven type that essentially benefits a lot from independent and stationary increments. The recursive representation, along with a few related ones, are derived by making use of a jump time of the underlying dynamics as an information relay point in passing the past on to a previous iteration step to fill in the missing information on the unobserved trajectory ahead. We prove that the proposed recursive representations are convergent, possibly slow in the pre-asymptotic regime yet exponentially fast in the limit, and can be represented in a similar form to Picard iterates under the probability measure with its jump component suppressed. On the basis of each iterate, we construct upper and lower bounding functions that are also convergent towards the true solution as the iterations proceed.
翻译:我们建立循环代表制, 完全分解从一大批多变性异异异异异异异异异异异异异异异异异异异异异异方方程式跳出, 跳出一般时间状态依赖无约束强度的跳跃, 而不是L\'evy- 驱动型跳跃, 基本上能从独立和固定的递增中大大受益。 循环代表制, 连同一些相关代表制, 是通过利用基本动态的跳动时间作为信息中继点, 跨过过去到以前的循环步骤, 填充关于未来未观测到的轨迹的缺失信息。 我们证明, 拟议的循环代表制会聚集在一起, 可能缓慢, 而在限值内, 快速快速地, 并且可以以类似的形式代表 Picard 的概率测量, 其跳动部分被抑制 。 我们根据每一次循环, 构建上下层的连接功能, 并且随着循环进程走向真正的解决方案 。