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Abstract The multiple-try Metropolis method is an interesting extension of the classical Metropolis–Hastings algorithm. However, theoretical understanding about its usefulness and convergence behavior is still lacking. We here derive the exact convergence rate for the multiple-try Metropolis Independent sampler (MTM-IS) via an explicit eigen analysis. As a by-product, we prove that an naive application of the MTM-IS is less efficient than using the simpler approach of “thinned” independent Metropolis–Hastings method at the same computational cost. We further explore more variants and find it possible to design more efficient algorithms by applying MTM to part of the target distribution or creating correlated multiple trials.
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This content will become publicly available on November 4, 2025
If At First You Don’t Succeed, Try, Try, Again...? Insights and LLM-informed Tooling for Detecting Retry Bugs in Software Systems
- Award ID(s):
- 2028427
- PAR ID:
- 10634705
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400712517
- Page Range / eLocation ID:
- 63 to 78
- Format(s):
- Medium: X
- Location:
- Austin TX USA
- Sponsoring Org:
- National Science Foundation
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