Probabilistic machine learning and decision making with Monte Carlo particle methods

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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.