308 research outputs found
Bernoulli Factories for Flow-Based Polytopes
We construct explicit combinatorial Bernoulli factories for the class of
\emph{flow-based polytopes}; integral 0/1-polytopes defined by a set of network
flow constraints. This generalizes the results of Niazadeh et al. (who
constructed an explicit factory for the specific case of bipartite perfect
matchings) and provides novel exact sampling procedures for sampling paths,
circulations, and -flows. In the process, we uncover new connections to
algebraic combinatorics
Perfect Sampling for Hard Spheres from Strong Spatial Mixing
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
Bernoulli Race Particle Filters
When the weights in a particle filter are not available analytically,
standard resampling methods cannot be employed. To circumvent this problem
state-of-the-art algorithms replace the true weights with non-negative unbiased
estimates. This algorithm is still valid but at the cost of higher variance of
the resulting filtering estimates in comparison to a particle filter using the
true weights. We propose here a novel algorithm that allows for resampling
according to the true intractable weights when only an unbiased estimator of
the weights is available. We demonstrate our algorithm on several examples.Comment: 19 page
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