Inference for Change-Point and Related Processes
Created: | 2014-01-02 10:05 |
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Institution: | Isaac Newton Institute for Mathematical Sciences |
Description: | 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 patient’s 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.
Read more at www.newton.ac.uk/programmes/ICP/ |
Media items
This collection contains 49 media items.
Media items
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
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 14 Feb 2014
Adaptive Spectral Estimation for Nonstationary Time Series
Stoffer, D (University of Pittsburgh)
Friday 17 January 2014, 11:30-12:15
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 24 Jan 2014
An algorithm to segment count data using a binomial negative model
Rigaill, G (INRA-CNRS-Université d'Evry Val d'Essonne, URGV)
Thursday 16 January 2014, 10:00-10:30
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 24 Jan 2014
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
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 24 Jan 2014
Analysis of time series observed on networks
Nunes, M (Lancaster University)
Wednesday 15 January 2014, 09:30-10:00
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 22 Jan 2014
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
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 12 Feb 2014
Bayesian inference in continuous time jump processes
Godsill, S (University of Cambridge)
Thursday 16 January 2014, 13:30-14:15
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 24 Jan 2014
Change-point detection and analysis
Siegmund, D (Stanford University)
Monday 13 January 2014, 14:00-15:00
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 15 Jan 2014
Change-point tests based on estimating functions
Kirch, C (Karlsruhe Institute of Technology)
Wednesday 15 January 2014, 13:30-14:00
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 22 Jan 2014
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
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 28 Jan 2014
Computationally Efficient Algorithms for Detecting Changepoints
Fearnhead, P (Lancaster University)
Thursday 16 January 2014, 09:30-10:00
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 24 Jan 2014
Decentralized Quickest Change Detection in Hidden Markov Models for Sensor Networks
Fuh, C-D (National Central University, Taiwan)
Wednesday 15 January 2014, 10:00-10:30
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 22 Jan 2014
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
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 12 Feb 2014
Detecting smooth changes in locally stationary processes
Vogt, M (University of Konstanz)
Tuesday 14 January 2014, 09:30-10:00
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 22 Jan 2014
Detection and Exploitation of Nonstationarities in Time Series Data
Nason, G (University of Bristol)
Monday 13 January 2014, 15:30-16:30
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 15 Jan 2014
Detection of Genomic Signals by Resequencing
Siegmund, D (Stanford University)
Monday 03 February 2014, 11:30-12:30
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 11 Feb 2014
Detection of multiple structural breaks in multivariate time series
Dette, H (Ruhr-Universität Bochum)
Tuesday 14 January 2014, 14:50-15:30
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 22 Jan 2014
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
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 12 Feb 2014
Fourier based statistics for irregular spaced spatial data: with an application to testing for spatial stationarity.
Subba Rao , S (Texas A&M University )
Friday 24 January 2014, 14:00-15:00
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 3 Feb 2014
Graph-Based Change-Point Detection
Chen, H (University of California, Davis)
Tuesday 21 January 2014, 11:30-12:30
Collection: Inference for Change-Point and Related Processes
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 3 Feb 2014