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Activity Number:
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472
- Junior Research in Bayesian Nonparametric Modeling of Complex or Unknown Populations
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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International Society for Bayesian Analysis (ISBA)
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Abstract #329550
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Title:
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Bayesian Non and Semiparametric Methods for Structured Sequential Data
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Author(s):
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Abhra Sarkar*
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Companies:
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University of Texas at Austin
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Keywords:
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Abstract:
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We are developing a broad array of novel Bayesian non and semiparametric frameworks for analyzing complex sequential data sets based on first and higher order Markovian dynamic. These methods help answer important scientific queries arising in a diverse field of applications. The methods have appealing theoretical properties and practical advantages, eliminate many limitations of previously existing methods and are of very broad utility with applications not limited to motivating examples. Our research also paves the way to advanced automated methods for many other sophisticated dynamical systems that can accommodate many different data types.
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Authors who are presenting talks have a * after their name.
