Hit-and run algorithms for the identification of nonredundant linear inequalities
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
On the best case performance of hit and run methods for detecting necessary constraints
Mathematical Programming: Series A and B
Hit-and-run algorithms for generating multivariate distributions
Mathematics of Operations Research
Direction Choice for Accelerated Convergence in Hit-And-Run Sampling
Operations Research
Monte-carlo techniques for falsification of temporal properties of non-linear hybrid systems
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
Approximating the tail of the Anderson-Darling distribution
Computational Statistics & Data Analysis
Combining time and frequency domain specifications for periodic signals
RV'11 Proceedings of the Second international conference on Runtime verification
Query strategies for evading convex-inducing classifiers
The Journal of Machine Learning Research
Probabilistic Temporal Logic Falsification of Cyber-Physical Systems
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on Probabilistic Embedded Computing
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The problem of efficiently generating general multivariate densities via a Monte Carlo procedure has experienced dramatic progress in recent years through the device of a Markov chain sampler. This procedure produces a sequence of random deviates corresponding to a random walk over the support of the target distribution. Under certain regularity conditions, the corresponding Markov chain converges in distribution to the target distribution. Thus the sample of points so generated can serve as a statistical sample of points drawn from the target distribution. A random walk that can globally reach across the support of the distribution in one step is called a Hit-and-Run sampler. Hit-and-Run Markov chain samplers offer the promise of faster convergence to the target distribution than conventional small step random walks. Applications to optimization are considered.