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BEGIN:VEVENT
DTSTAMP:20260427T084121Z
DTSTART;VALUE=DATE-TIME:20180126T100000
DTEND;VALUE=DATE-TIME:20180126T140000
SUMMARY:OxWaSP
TZID:Europe/London
UID:20180126-8a17841b5e5cebca015e7141d71b42d3@warwick.ac.uk
CREATED:20170911T140453Z
DESCRIPTION:module 5: 26 January organised by Jim Smith (ĢĒŠÄTV) - FranƧ
 ois Caron (Oxford) 1400-1500 Mihaela van der Schaar (Oxford Man) AutoPro
 gnosis Mihaela's work uses data science and machine learning to create m
 odels that assist diagnosis and prognosis. Existing models suffer from t
 wo kinds of problems. Statistical models that are driven by theory/hypot
 heses are easy to apply and interpret but they make many assumptions and
  often have inferior predictive accuracy. Machine learning models can be
  crafted to the data and often have superior predictive accuracy but the
 y are often hard to interpret and must be crafted for each disease ā€¦ and
  there are a lot of diseases. In this talk I present a method (AutoProgn
 osis) that makes machine learning itself do both the crafting and interp
 reting. For medicine\, this is a complicated problem because missing dat
 a must be imputed\, relevant features/covariates must be selected\, and 
 the most appropriate classifier(s) must be chosen. Moreover\, there is n
 o one ā€œbestā€ imputation algorithm or feature processing algorithm or cla
 ssification algorithm\; some imputation algorithms will work better with
  a particular feature processing algorithm and a particular classifier i
 n a particular setting. To deal with these complications\, we need an en
 tire pipeline. Because there are many pipelines we need a machine learni
 ng method for this purpose\, and this is exactly what AutoPrognosis is: 
 an automated process for creating a particular pipeline for each particu
 lar setting. Using a variety of medical datasets\, we show that AutoProg
 nosis achieves performance that is significantly superior to existing cl
 inical approaches and statistical and machine learning methods. 1530-163
 0 Jim Griffith (Kent) Bayesian nonparametric vector autoregressive model
 s Vector autoregressive (VAR) models are the main work-horse model for m
 acroeconomic forecasting\, and provide a framework for the analysis of c
 omplex dynamics that are present between macroeconomic variables. Whethe
 r a classical or a Bayesian approach is adopted\, most VAR models are li
 near with Gaussian innovations. This can limit the modelā€™s ability to ex
 plain the relationships in macroeconomic series. We propose a nonparamet
 ric VAR model that allows for nonlinearity in the conditional mean\, het
 eroscedasticity in the conditional variance\, and non-Gaussian innovatio
 ns. Our approach differs to that of previous studies by modelling the st
 ationary and transition densities using Bayesian nonparametric methods. 
 Our Bayesian nonparametric VAR (BayesNP-VAR) model is applied to US and 
 UK macroeconomic time series\, and compared to other Bayesian VAR models
 . We show that BayesNP-VAR is a flexible model that is able to account f
 or nonlinear relationships as well as heteroscedasticity in the data. In
  terms of short-run out-of-sample forecasts\, we show that BayesNP-VAR p
 redictively outperforms competing models.
LOCATION:C0.08
CATEGORIES:
LAST-MODIFIED:20180119T122631Z
ORGANIZER;CN=David Kinmond:
END:VEVENT
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