Computational Probability

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The main research of the group can be summarized as follows: the design, analysis and application of stochastic algorithms for data analysis and modelling. What does this mean? Well, given some real problem, such as the prediction of the index of the stock market, one constructs a statistical or stochastic model which hopes to capture the real-life phenomenon.  In practice, these models can be quite intricate and they often lead to solutions that are not analytically tractable. That is to say, that one cannot return the `answer’ e.g. a prediction of a stock index, using a simple mathematical formula which can be computed given the data. Often, one has to resort to numerical approximation schemes using probabilistic techniques and this is where our work is done. In some cases, our work is to design new, efficient and scalable algorithms, which are mathematically understood, for real problems in finance or biology and health. In other cases, it is to provide mathematical understanding of existing, modern work, so as to comprehend the utility of this given algorithm. In other cases, one would like to apply existing algorithms to real case studies, so as to understand a given data generating mechanism.