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SUMMARY:Modern Bayesian machine learning methods and their application to 
 finance and econometrics - Ghahramani\, Z (University of Cambridge)
DTSTART:20131119T115000Z
DTEND:20131119T124000Z
UID:TALK48895@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Uncertainty\, data\, and inference play a fundamental role in 
 modelling. Probabilistic approaches to modelling have transformed scientif
 ic data analysis\, artificial intelligence and machine learning\, and have
  made it possible to exploit the many opportunities arising from the recen
 t explosion of big data problems arising in the sciences\, society and com
 merce. Once a probabilistic model is defined\, Bayesian statistics (which 
 used to be called "inverse probability") can be used to make inferences an
 d predictions from the model. Bayesian methods work best when they are app
 lied to models that are flexible enough to capture the complexity of real-
 world data. Recent work on non-parametric Bayesian machine learning provid
 es this flexibility. \n\nI will give an overview of some of our recent wor
 k in nonparametric Bayesian modelling\, with an emphasis on models that mi
 ght be useful in computerised trading\, finance and econometrics. Some top
 ics I will cover include scalable and interpretable time series forecastin
 g with Gaussian process regression models\, modelling switching and non-st
 ationarity in time series with infinite HMMs\, and multivariate stochastic
  volatility via Wishart processes and dynamic covariance models. \n
LOCATION:Seminar Room 2\, Newton Institute Gatehouse
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