自动后向滤波前向导引简介
Introduction to Automatic Backward Filtering Forward Guiding
摘要：
在本文中，我的目的是给一个非正式的治疗自动向后过滤正向引导，一般算法的条件采样从一个马尔可夫过程的有向无环图。我将说明，基本的想法可以理解的概率和统计学的基本背景。更技术性的处理是论文自动向后过滤马尔可夫过程和图形模型的前向指导（Van der Meulen和Schauer，2021）。我特别假设一些基于似然推理和贝叶斯统计的背景知识。最后一节要求更高，假设熟悉从无穷小生成器构造的连续时间随机过程。显然，这里讨论的所有工作都是过去十年与各种合作者一起进行的研究的结果，最重要的是Moritz Schauer（瑞典查尔姆斯理工大学和哥德堡大学）。第8部分是基于与Marcin Mider（Trium Analysis Online GmbH，德国）和Frank Schäfer（巴塞尔大学，瑞士）的联合工作。
英文摘要：
In this document I aim to give an informal treatment of automatic Backward Filtering Forward Guiding, a general algorithm for conditional sampling from a Markov process on a directed acyclic graph. I'll show that the underlying ideas can be understood with a basic background in probability and statistics. The more technical treatment is the paper Automatic backward filtering forward guiding for Markov processes and graphical models (Van der Meulen and Schauer, 2021). I specifically assume some background knowledge on likelihood based inference and Bayesian statistics. The final sections are more demanding and assume familiarity with continuous-time stochastic processes constructed from their infinitesimal generator. Clearly, all work discussed here is the result of research carried out over the past decade together with various collaborators, most importantly Moritz Schauer (Chalmers University of Technology and University of Gothenburg, Sweden). Section 8 is based on joint work with Marcin Mider (Trium Analysis Online GmbH, Germany) and Frank Schäfer (University of Basel, Switzerland) as well.
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