Advanced Monte Carlo Methods for Inference in Complex Dynamic Models

许多物理现象和大量数据可以用统计模型或概率模型精确地建模。例子包括股票市场的波动、基因表达、雷达信号、关系数据或图像。然而，即使有可能获得真实的物理模型或令人满意的统计模型，如果不使用数值方法，也只能解决最简单的问题。需要这种数值方法的例子包括非线性非高斯时间序列模型、马尔可夫随机场、社会网络模型等。由于巨大而廉价的计算能力的出现和大量复杂推理机制的发展，现在有可能，并且在许多现实世界的系统中，使用复杂的基于模拟的技术来提供以前认为无法解决的问题的解决方案变得越来越普遍。本文讨论的研究计划背后的意图是扩展当前和设计新颖的基于仿真的体系结构，以攻击和有效地解决仍然被认为难以解决的问题。

Many physical phenomena and much data can be accurately modelled using statistical or proba-bilistic models. Examples include the volatility of the stock market, gene expressions, radar signals,relational data or images. However, even if it is possible to obtain realistic physical models or satisfactory statistical models, only the simplest can be solved without the use of numerical methods.Examples of the need for such numerical methods include non-linear non-Gaussian time series models,Markov random fields, social networks models and so on. Thanks to the advent of enormous,cheap computational power and the development of a plethora of complex inference mechanisms, itis now possible, and in many real world systems it is becoming increasingly common, to employsophisticated simulation-based techniques to provide solutions to problems previously deemed insoluble. The intention behind the research program discussed herein is to extend current, and devisenovel, simulation-based architectures to attack and efficiently solve problems that are still deemedintractable.