We give a algorithm for exact sampling from the Bingham distribution p(x)∝exp(x⊤Ax) on the sphere Sd−1 with expected runtime of poly(d,λmax(A)−λmin(A)). The algorithm is based on rejection sampling, where the proposal distribution is a polynomial approximation of the pdf, and can be sampled from by explicitly evaluating integrals of polynomials over the sphere. Our algorithm gives exact samples, assuming exact computation of an inverse function of a polynomial. This is in contrast with Markov Chain Monte Carlo algorithms, which are not known to enjoy rapid mixing on this problem, and only give approximate samples.
As a direct application, we use this to sample from the posterior distribution of a rank-1 matrix inference problem in polynomial time.
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Leibniz International Proceedings in Informatics (LIPIcs):Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)
We provide a perfect sampling algorithm for the hard-sphere model on subsets of R^d with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling algorithms have been devised to sample from the hard-sphere model, and our perfect sampling algorithm is efficient for a range of parameters for which only efficient approximate samplers were previously known and is faster than these known approximate approaches. Our methods also extend to the more general setting of Gibbs point processes interacting via finite-range, repulsive potentials.
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- Award ID(s):
- 2309708
- NSF-PAR ID:
- 10501357
- Editor(s):
- Megow, Nicole; Smith, Adam
- Publisher / Repository:
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Date Published:
- Subject(s) / Keyword(s):
- perfect sampling hard-sphere model Gibbs point processes Theory of computation → Randomness, geometry and discrete structures
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2023.38
