Modern Bayesian machine learning methods and their application to finance and econometrics
Seminar Room 2, Newton Institute Gatehouse
AbstractUncertainty, data, and inference play a fundamental role in modelling. Probabilistic approaches to modelling have transformed scientific data analysis, artificial intelligence and machine learning, and have made it possible to exploit the many opportunities arising from the recent explosion of big data problems arising in the sciences, society and commerce. Once a probabilistic model is defined, Bayesian statistics (which used to be called "inverse probability") can be used to make inferences and predictions from the model. Bayesian methods work best when they are applied to models that are flexible enough to capture the complexity of real-world data. Recent work on non-parametric Bayesian machine learning provides this flexibility.
I will give an overview of some of our recent work in nonparametric Bayesian modelling, with an emphasis on models that might be useful in computerised trading, finance and econometrics. Some topics I will cover include scalable and interpretable time series forecasting with Gaussian process regression models, modelling switching and non-stationarity in time series with infinite HMMs, and multivariate stochastic volatility via Wishart processes and dynamic covariance models.