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Activity Number:
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165
- Bayesian Methods in Structured Data and High-Dimensional Problem: Some Recent Advances
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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International Society for Bayesian Analysis (ISBA)
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Abstract #312634
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Title:
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New Directions in Bayesian Change-Point Detection in High Dimension
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Author(s):
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Nilabja Guha*
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Companies:
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University of Massachusetts, Lowell
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Keywords:
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Abstract:
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Hierarchical models with structural change-points are routinely observed across many scientific disciplines. They arise naturally in countless applications where the mean or dependence structure changes depending on exogenous covariates. In higher dimensions, the problem of detecting change-points and the changing structure is often rendered extremely difficult owing to a combinatorial computational complexity. Here we propose consistent Bayesian change point detection methodology for high dimension.
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Authors who are presenting talks have a * after their name.
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