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Tue 6 Jan, '15
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YRM
C0.06, Common Room
Wed 7 Jan, '15
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Dept Council Meeting
Radcliffe House

plus lunch

Wed 7 Jan, '15
-
SF@W Reading Group
C1.06
Wed 7 Jan, '15
-
Taught SSLC
C1.06
Thu 8 Jan, '15
-
P@W Reading Group
C0.08
Thu 8 Jan, '15
-
Neuro Stats Reading Group
C1.06
Fri 9 Jan, '15
-
Algorithms & Computationally Intensive Inference Seminars
C1.06
Mon 12 Jan, '15
-
OxWaSP Interviews
C0.06, Common Room
Tue 13 Jan, '15
-
YRM
C0.06, Common Room
Wed 14 Jan, '15
-
MG Meeting
C1.06
Wed 14 Jan, '15
-
SF@W Reading Group
C1.06
Wed 14 Jan, '15
-
Teaching Committee
C1.06
Thu 15 Jan, '15
-
P@W Reading Group
C0.08
Thu 15 Jan, '15
-
Neuro Stats Reading Group
C1.06
Fri 16 Jan, '15
-
Markov Chains Reading Group
C1.06
Fri 16 Jan, '15
-
Algorithms & Computationally Intensive Inference Seminars
C1.06
Tue 20 Jan, '15
-
YRM
C0.06, Common Room
Wed 21 Jan, '15
-
SF@W Reading Group
C1.06
Wed 21 Jan, '15
-
Midlands Probability Theory Seminar
A1.01 & B3.02
Wed 21 Jan, '15
-
PCAPP Event: Teaching Forum
D1.07

In the Teaching Forum established staff members will share their experience of teaching in the Mathematical Sciences. This time the speakers (well known to most of us!) are Dr David Wood, Director of Undergraduate Studies in the Department of Mathematics, and Dr Jon Warren, current Deputy Head responsible for Teaching and Learning in the Department of Statistics.

Thu 22 Jan, '15
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P@W Reading Group
C0.08
Thu 22 Jan, '15
-
Neuro Stats Reading Group
C1.06
Thu 22 Jan, '15
-
WCC Meeting
C1.06
Fri 23 Jan, '15
-
Markov Chains Reading Group
C1.06
Fri 23 Jan, '15
-
Algorithms & Computationally Intensive Inference Seminars
C1.06
Fri 23 Jan, '15
-
CRiSM Seminar - Rebecca Killick (Lancaster), Peter Green (Bristol)
B1.01 (Maths)

Rebecca Killick (Lancaster)
Forecasting locally stationary time series
Within many fields forecasting is an important statistical tool. Traditional statistical techniques often assume stationarity of the past in order to produce accurate forecasts. For data arising from the energy sector and others, this stationarity assumption is often violated but forecasts still need to be produced. This talk will highlight the potential issues when moving from forecasting stationary to nonstationary data and propose a new estimator, the local partial autocorrelation function, which will aid us in forecasting locally stationary data. We introduce the lpacf alongside associated theory and examples demonstrating its use as a modelling tool. Following this we illustrate the new estimator embedded within a forecasting method and show improved forecasting performance 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 relationships that hold among the variables, often represented graphically, and inferring these from data is an important step in understanding a complex stochastic system. We would like to make simultaneous inference about the conditional independence graph and parameters of the model; this 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 consequences for prior modelling, and discuss a new MCMC algorithm for sampling graphs that enables Bayesian structural and quantitative learning on a much bigger scale than previously possible. This is joint work with Alun Thomas (Utah).

Mon 26 Jan, '15
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Assistant Professors - Candidate Presentations
CS0.07 (Computer Science)
Tue 27 Jan, '15
-
YRM
C0.06, Common Room
Wed 28 Jan, '15
-
MG Meeting
C0.19 (Mark's office)
Wed 28 Jan, '15
-
SF@W Seminar
A1.01

Rama Cont

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