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BEGIN:VEVENT
DTSTAMP:20260426T131941Z
DTSTART;VALUE=DATE-TIME:20150123T140000
DTEND;VALUE=DATE-TIME:20150123T160000
SUMMARY:CRiSM Seminar - Rebecca Killick (Lancaster)\, Peter Green (Bristo
 l)
TZID:Europe/London
UID:20150123-094d43f546af57eb0146b8c8817670fe@warwick.ac.uk
CREATED:20140620T101806Z
DESCRIPTION:Rebecca Killick (Lancaster) Forecasting locally stationary ti
 me series Within many fields forecasting is an important statistical too
 l. Traditional statistical techniques often assume stationarity of the p
 ast in order to produce accurate forecasts. For data arising from the en
 ergy sector and others\, this stationarity assumption is often violated 
 but forecasts still need to be produced. This talk will highlight the po
 tential issues when moving from forecasting stationary to nonstationary 
 data and propose a new estimator\, the local partial autocorrelation fun
 ction\, which will aid us in forecasting locally stationary data. We int
 roduce the lpacf alongside associated theory and examples demonstrating 
 its use as a modelling tool. Following this we illustrate the new estima
 tor embedded within a forecasting method and show improved forecasting p
 erformance using this new technique. Peter Green (Bristol) Inference on 
 decomposable graphs: priors and sampling The structure in a multivariate
  distribution is largely captured by the conditional independence relati
 onships that hold among the variables\, often represented graphically\, 
 and inferring these from data is an important step in understanding a co
 mplex stochastic system. We would like to make simultaneous inference ab
 out the conditional independence graph and parameters of the model\; thi
 s is known as joint structural and quantitative learning in the machine 
 learning literature. The Bayesian paradigm allows a principled approach 
 to this simultaneous inference task. There are tremendous computational 
 and interpretational advantages in assuming the conditional independence
  graph is decomposable\, and not too many disadvantages. I will present 
 a new structural Markov property for decomposable graphs\, show its cons
 equences for prior modelling\, and discuss a new MCMC algorithm for samp
 ling graphs that enables Bayesian structural and quantitative learning o
 n a much bigger scale than previously possible. This is joint work with 
 Alun Thomas (Utah).
LOCATION:B1.01 (Maths)
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20140620T101806Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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