Compression of samplable sources
Computational Complexity
Random walk based node sampling in self-organizing networks
ACM SIGOPS Operating Systems Review
Coupling and self-stabilization
Distributed Computing - Special issue: DISC 04
Convergence rates of Markov chains for some self-assembly and non-saturated Ising models
Theoretical Computer Science
The Glauber Dynamics for Colourings of Bounded Degree Trees
APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Progresses in the analysis of stochastic 2D cellular automata: A study of asynchronous 2D minority
Theoretical Computer Science
Probabilistic structured predictors
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
On probabilistic fixpoint and Markov chain query languages
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques
Dichotomy for Holant problems of Boolean domain
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Progresses in the analysis of stochastic 2D cellular automata: a study of asynchronous 2D minority
MFCS'07 Proceedings of the 32nd international conference on Mathematical Foundations of Computer Science
Beam search algorithms for multilabel learning
Machine Learning
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In this tutorial, we introduce the notion of a Markov chain and explore how it can be used to sample from a large set of configurations. Our primary focus is determining how quickly a Markov chain "mixes," or converges to its stationary distribution, as this is the key factor in the running time. We provide an overview of several techniques used to establish good bounds on the mixing time. The applications are mostly chosen from statistical physics, although the methods are much more general.