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Lectures on Monte Carlo Methods
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Introduction Generating random numbers Variance reduction techniques Markov chain Monte Carlo statistical analysis of simulation output and the Ising model.Abstract:
Introduction Generating random numbers Variance reduction techniques Markov chain Monte Carlo Statistical analysis of simulation output The Ising model and related examples Bibliography.read more
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Journal ArticleDOI
High-Order Collocation Methods for Differential Equations with Random Inputs
Dongbin Xiu,Jan S. Hesthaven +1 more
TL;DR: A high-order stochastic collocation approach is proposed, which takes advantage of an assumption of smoothness of the solution in random space to achieve fast convergence and requires only repetitive runs of an existing deterministic solver, similar to Monte Carlo methods.
Proceedings Article
Towards efficient sampling: exploiting random walk strategies
TL;DR: It is shown that random walk SAT procedures often do reach the full set of solutions of complex logical theories and how the sampling becomes near-uniform is shown.
Journal ArticleDOI
Markov Random Field Model-Based Edge-Directed Image Interpolation
Min Li,Truong Q. Nguyen +1 more
TL;DR: This paper presents an edge-directed image interpolation algorithm that improves the subjective quality of the interpolated edges while maintaining a high PSNR level and a single-pass implementation is designed, which performs nearly as well as the iterative optimization.
Proceedings Article
Distribution-aware sampling and weighted model counting for SAT
TL;DR: A novel parameter, tilt, is identified, which is the ratio of the maximum weight of satisfying assignment to minimum weight of satisfies assignment, and a novel approach is presented that works with a black-box oracle for weights of assignments and requires only an NP-oracle to solve both the counting and sampling problems when the tilt is small.
Proceedings Article
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints
TL;DR: This work proposes a new technique for sampling the solutions of combinatorial problems in a near-uniform manner, focusing on problems specified as a Boolean formula, i.e., on SAT instances, and achieves a significantly better sampling quality than the best alternative.
Related Papers (5)
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Mark Jerrum,Alistair Sinclair +1 more