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Activity Number:
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541
- Recent Progresses in Bayesian Inference in Large Parameter Spaces: Jayanta K. Ghosh Memorial Session
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Type:
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Invited
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Date/Time:
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Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Memorial
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Abstract #300433
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Presentation
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Title:
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Leveraging the Order-Dependence of Predictive Recursion for Uncertainty Quantification About a Mixing Density
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Author(s):
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Ryan Martin* and Vaidehi Dixit
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Companies:
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North Carolina State University and North Carolina State University
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Keywords:
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mixture model;
nonparametric;
density estimation;
deconvolution;
uncertainty quantification;
inverse problem
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Abstract:
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Estimation of the mixing distribution based on data coming from a mixture model is a challenging problem, and quantification of certainty is all the more difficult. In this talk, I will present a strategy that leverages the order-dependence of Newton's predictive recursion estimator to construct a permutation-based posterior distribution and discuss -- theoretically and empirically -- its uncertainty quantification properties.
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Authors who are presenting talks have a * after their name.
