Probabilistic machine learning and decision making with Monte Carlo particle methods

2001年，我的NSERC提案一开始就指出，对文本、视频、多媒体、基因数据和环境信号等大量数据进行建模，是新世纪人工智能和统计学领域最艰巨的挑战之一。当时，我指出，为了为数据构建合适的模型，我们需要与不确定性作斗争，因为数据通常是嘈杂的、不精确的，而且只能部分观察到。这种不确定性，再加上问题的严重性，使得仅从专家那里获得的知识无法构建模型。为了克服这些问题，我提出使用概率学习技术和蒙特卡罗算法从数据中自动推断模型。

In 2001, my NSERC proposal began by stating that the modelling of vast amounts of data, such as text, video, multimedia, gene data and environmental signals, was one of the most demanding challenges of artificial intelligence and statistics in the new century. At the time, I pointed out that to construct appropriate models for the data, we needed to contend with uncertainty as data is typically noisy, imprecise and only partially observed. This uncertainty, coupled with the sheer magnitude of the problem, precluded the construction of models using knowledge acquired solely from experts. To overcome these problems, I proposed the use of probabilistic learning techniques and Monte Carlo algorithms to infer the models automatically from data.