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Activity Number:
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253
- Contributed Poster Presentations: Section on Statistical Computing
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #330726
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Title:
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Continuous Tempering Through Path Sampling
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Author(s):
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Yuling Yao* and Andrew Gelman
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Companies:
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Columbia Univ and Columbia University
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Keywords:
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Bayesian computation;
Hamiltonian Monte Carlo;
simulated tempering;
path sampling;
normalizing constant
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Abstract:
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Hamiltonian Monte Carlo may face challenges when sampling from a multi-modal distribution. We propose a new tempering algorithm by introducing a continuous auxiliary temperature parameter and conduct joint HMC jumps in the joint space. The path sampling gives a smoothed estimation of normalizing constant, while the adaptive procedure makes more reliable estimation for the partition function of the entire path. We compare the results with other tempering methods and find the proposed approach to be optimal in simulation examples.
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Authors who are presenting talks have a * after their name.
