论文标题
自动向后滤波向前指导的简介
Introduction to Automatic Backward Filtering Forward Guiding
论文作者
论文摘要
在本文档中，我旨在对自动向后过滤前向导进行非正式处理，这是一种从马尔可夫过程中的有条件采样的一般算法，以有针对性的无环图。我将证明可以从概率和统计数据的基本背景来理解基础想法。技术处理越多是纸自动向后过滤前向马尔可夫工艺和图形模型的向前指导（van der Meulen and Schauer，2021）。我特别假设一些有关基于可能性的推理和贝叶斯统计的背景知识。最终部分的要求更高，并假定熟悉其无限发电机构建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中与各种合作者（最重要的是莫里茨·舒尔（Moritz Schauer）（查尔默斯技术大学和瑞典哥德堡大学）进行的研究的结果。第8节基于与Marcin Mider（德国Trium Analysis在线分析）和FrankSchäfer（瑞士巴塞尔大学）的联合合作。
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.
