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Activity Number:
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246
- Bayesian Nonparametrics
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #304657
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Title:
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Efficient Bayesian Shape-Constrained Function Estimation
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Author(s):
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Pallavi Ray* and Debdeep Pati and Anirban Bhattacharya
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Companies:
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Texas A&M University - College Station and Texas A&M University and TAMU
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Keywords:
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Circulant embedding;
Durbin’s recursion;
Elliptical Slice Sampling;
Smooth relaxation;
Toeplitz
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Abstract:
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We revisits the problem of Bayesian shape-restricted inference in the light of a recently developed approximate Gaussian process that admits an equivalent formulation of the shape constraints in terms of the basis coefficients. We propose a strategy to efficiently sample from the resulting constrained posterior by absorbing a smooth relaxation of the constraint in the likelihood and using circulant embedding techniques to sample from the unconstrained modified prior. We additionally pay careful attention to mitigate the computational complexity arising from updating hyperparameters within the covariance kernel of the Gaussian process. The developed algorithm is shown to be accurate and highly efficient in simulated and real data examples.
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Authors who are presenting talks have a * after their name.
