The hit-and-run sampler: a globally reaching markov chain sampler for generating arbitrary multivariate distributions

By Robert L. Smith. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Steven J. Lee
2. Satish Rao
3. Shing-Hon Lau
4. Anthony Tran
5. Blaine Nelson
6. Battista Biggio

Citations in the corpus

• Query Strategies for Evading Convex-Inducing Classifiers (2010, methodology): “The backbone of the algorithm is the capability to sample uniformly from an unknown but bounded convex body by means of the hit-and-run random walk technique introduced by Smith (1996) (Algorithm 7).”
• Near-Optimal Evasion of Convex-Inducing Classifiers (2010, methodology): “The backbone of the algorithm is uniform sampling from a bounded convex body by means of the hitand-run random walk technique introduced by Smith (1996) (Algorithm 3.”