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...
Latest episodes
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30 min ↗
A primal dual method for inverse problems in MRI with non-linear forward operators
Valkonen, T (University of Cambridge) Friday 07 February 2014, 14:30-15:00
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57 min ↗
Applications of Change-Points Methods in Brain Signal and Image Analysis
Ombao, H (University of California, Irvine) Wednesday 05 February 2014, 11:30-12:30
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59 min ↗
Wavelet-based Bayesian Estimation of Long Memory Models - an Application to fMRI Data
Vannucci, M (Rice University) Tuesday 04 February 2014, 14:00-15:00
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60 min ↗
Detecting copy number variants for rare genetic disorders and non-invasive pre-natal diagnosis
Plagnol, V (University College London) Tuesday 04 February 2014, 11:30-12:30
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53 min ↗
Methods for detecting graph based change points for fMRI and financial data
Cribben, I (University of Alberta) Wednesday 05 February 2014, 10:00-11:00
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54 min ↗
Exact Bayesian inference for change point models with application to genomics
Robin, S (INRA - Institut National de la Recherche Agronomique) Monday 03 February 2014, 14:00-15:00
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1 h 01 ↗
Detection of Genomic Signals by Resequencing
Siegmund, D (Stanford University) Monday 03 February 2014, 11:30-12:30
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1 h 01 ↗
Theory and Inference for a Class of Nonlinear Models with Application to Time Series of Counts.
Davis, R (Columbia University) Thursday 30 January 2014, 11:30-12:30
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1 h 07 ↗
Modeling spatial nonstationarity and inference for exceedances in environmental applications.
Craigmile, P (Ohio State University) Tuesday 28 January 2014, 11:30-12:30
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1 h 04 ↗
Incorporating Geostrophic Wind Information for Improved Space-Time Short-Term Wind Speed Forecasting and Power…
Genton, M (King Abdullah University of Science and Technology (KAUST)) Wednesday 29 January 2014, 11:30-12:30
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1 h 09 ↗
Graph-Based Change-Point Detection
Chen, H (University of California, Davis) Tuesday 21 January 2014, 11:30-12:30
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1 h 01 ↗
Fourier based statistics for irregular spaced spatial data: with an application to testing for spatial station…
Subba Rao , S (Texas A&M University ) Friday 24 January 2014, 14:00-15:00
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1 h 05 ↗
Robust monitoring of CAPM portfolios beta
Husková, M (Charles University, Prague) Wednesday 22 January 2014, 11:30-12:30
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1 h 01 ↗
Measuring dependence with local Gaussian correlation: Theory and applications.
Tjøstheim, D (Universitetet i Bergen) Thursday 23 January 2014, 11:30-12:30
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1 h 02 ↗
Statistical Inference for Panel Data
Horvath, L (University of Utah) Friday 24 January 2014, 12:00-13:00
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47 min ↗
The Volatility of International Migration Flows: Estimating Past Trends in Nordic Countries and Lessons for Fo…
Abel, G (Austrian Academy of Sciences) Tuesday 21 January 2014, 14:00-15:00
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1 h 12 ↗
Characterizing, predicting and handling rapid and large changes of wind power production.
Girard, R (Mines Paris Tech) Monday 27 January 2014, 15:10-16:10
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26 min ↗
Precision of Disorders Detection
Szajowski, K (Wroclaw University of Technology) Wednesday 15 January 2014, 16:30-17:00
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1 h 00 ↗
An Automated Statistician which learns Bayesian nonparametric models of time series data
Ghahramani, Z (University of Cambridge) Thursday 16 January 2014, 14:15-15:00
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47 min ↗
The group fused Lasso for multiple change-point detection
Vert, J-P (Mines ParisTech) Friday 17 January 2014, 09:30-10:15