{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/299413"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/299413","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Bayesian Time Series Learning with Gaussian Processes","abstract":"The analysis of time series data is important in fields as disparate as the social sciences, biology, engineering or econometrics. In this dissertation, we present a number of algorithms designed to learn Bayesian nonparametric models of time series. The goal of these kinds of models is twofold. First, they aim at making predictions which quantify the uncertainty due to limitations in the quantity and the quality of the data. Second, they are flexible enough to model highly complex data whilst preventing overfitting when the data does not warrant complex models. 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