ICP
13 January 2014 to 7 February 2014
In many applications data is collected over time or can be ordered with respect to some other criteria (e.g. position along a chromosome). Often the statistical properties, such as mean or variance, of the data will change along data. This feature of data is known as non-stationarity. An important and challenging problem is to be able to model and infer how these properties change. Examples occur in environmental applications (e.g. detecting changes in ecological systems due to climatic conditions crossing some critical thresholds), signal processing (e.g. structural analysis of EEG signals), epidemiology (e.g. early detection of hospital infections from changes in patients antibody levels), bioinformatics (e.g. detecting changes in copy number variation), and finance (e.g. changing volatility). As technology advances, and ever larger and complex data are collected, the need to model changes in the statistical properties of the data, and the difficulty of making inference for these models increases.
Two possibilities for modelling non-stationarity are changepoint models and locally-stationary models. The former splits data into segments, where the statistical properties of the data is the same within a segment, but changes between segments. These are often appropriate for applications with abrupt changes. By comparison, locally-stationary models are based on stochastic processes where the statistical properties vary more smoothly. These two approaches are thus complementary. To date research within both areas is generally carried out independently of the other.
The main themes of the programme will be:
A focus of the programme will be the application of these methods, particularly within finance, energy and environment, and biomedical sciences.
Click here to download the programme's final scientific report
Title | Year | Programme | |
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Automatic construction and natural-language description of nonparametric regression modelsAuthors: JR Lloyd, D Duvenaud, R Grosse, JB Tenenbaum, Zoubin Ghahramani |
2013 | ICP | 21 October 2016 |
On optimal multiple changepoint algorithms for large dataAuthors: Rob Maidstone, T Hocking, Guillem Rigaill, P Fearnhead |
2013 | ICP | 21 October 2016 |
Change points in high dimensional settingsAuthors: John Aston, Claudia Kirch |
2013 | ICP | 21 October 2016 |
On some distributed disorder detectionAuthors: Krzysztof Szajowski |
2013 | ICP | 21 October 2016 |
13 January 2014 to 17 January 2014
Monday 13th January 2014 | |||
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10:00 to 11:30 | No Room Required | ||
11:30 to 12:30 | Room 1 | |
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12:30 to 13:30 | No Room Required | ||
15:00 to 15:30 | No Room Required | ||
15:30 to 16:30 |
Detection and Exploitation of Nonstationarities in Time Series Data |
Room 1 | |
16:30 to 18:00 | No Room Required |
Wednesday 15th January 2014 | |||
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10:30 to 11:00 |
Nonparametric change-point detection with sparse alternatives |
Room 1 | |
11:00 to 11:30 | No Room Required | ||
11:30 to 12:15 | Room 1 | |
|
12:15 to 13:30 | No Room Required | ||
15:00 to 15:30 | No Room Required | ||
19:30 to 22:00 |
Conference Dinner at Cambridge Union Society hosted by Cambridge Dining Company |
No Room Required |
Thursday 16th January 2014 | |||
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11:00 to 11:30 | No Room Required | ||
11:30 to 12:15 | Room 1 | ||
12:15 to 13:30 | No Room Required | ||
13:30 to 14:15 | Room 1 | |
|
14:15 to 15:00 |
An Automated Statistician which learns Bayesian nonparametric models of time series data |
Room 1 | |
15:00 to 15:30 | No Room Required | ||
15:50 to 16:10 | Room 1 | |
Thursday 23rd January 2014 | |||
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11:30 to 12:30 |
Measuring dependence with local Gaussian correlation: Theory and applications. |
Room 2 | |
Monday 27th January 2014 | |||
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14:00 to 15:00 | Room 1 | ||
15:10 to 16:10 |
Characterizing, predicting and handling rapid and large changes of wind power production. |
Room 1 | |
Tuesday 28th January 2014 | |||
---|---|---|---|
11:30 to 12:30 |
Modeling spatial nonstationarity and inference for exceedances in environmental applications. |
Room 2 | |
Thursday 30th January 2014 | |||
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11:30 to 12:30 |
Theory and Inference for a Class of Nonlinear Models with Application to Time Series of Counts. |
Room 2 | |
Monday 3rd February 2014 | |||
---|---|---|---|
11:30 to 12:30 | Room 2 | |
|
14:00 to 15:00 |
Exact Bayesian inference for change point models with application to genomics |
Room 2 | |
Tuesday 4th February 2014 | |||
---|---|---|---|
11:30 to 12:30 |
Detecting copy number variants for rare genetic disorders and non-invasive pre-natal diagnosis |
Room 2 | |
14:00 to 15:00 |
Wavelet-based Bayesian Estimation of Long Memory Models - an Application to fMRI Data |
Room 2 | |
Wednesday 5th February 2014 | |||
---|---|---|---|
10:00 to 11:00 |
Methods for detecting graph based change points for fMRI and financial data |
Room 2 | |
11:30 to 12:30 |
Applications of Change-Points Methods in Brain Signal and Image Analysis |
Room 2 | |
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