
Polynomialtime approximation algorithms for the antiferromagnetic Ising model on line graphs
We present a polynomialtime Markov chain Monte Carlo algorithm for esti...
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Polynomial Time Algorithms for Dual Volume Sampling
We study dual volume sampling, a method for selecting k columns from an ...
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Approximating the Permanent by Sampling from Adaptive Partitions
Computing the permanent of a nonnegative matrix is a core problem with ...
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Mergesplit Markov chain Monte Carlo for community detection
We present a Markov chain Monte Carlo scheme based on merges and splits ...
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Efficient posterior sampling for highdimensional imbalanced logistic regression
Highdimensional data are routinely collected in many application areas....
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Geometric nested sampling: sampling from distributions defined on nontrivial geometries
Metropolis Hastings nested sampling evolves a Markov chain, accepting ne...
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The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications
While the Matrix Generalized Inverse Gaussian (MGIG) distribution arises...
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Efficient sampling from the Bingham distribution
We give a algorithm for exact sampling from the Bingham distribution p(x)∝(x^⊤ A x) on the sphere 𝒮^d1 with expected runtime of poly(d, λ_max(A)λ_min(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 rank1 matrix inference problem in polynomial time.
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