Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
The Swendsen-Wang process does not always mix rapidly
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Approximating the permanent via importance sampling with application to the dimer covering problem
Journal of Computational Physics
IEEE Computational Science & Engineering
Computing in Science and Engineering
Rapidly Mixing Markov Chains with Applications in Computer Science and Physics
Computing in Science and Engineering
Simple Monte Carlo and the Metropolis algorithm
Journal of Complexity
Bayesian system identification via Markov chain Monte Carlo techniques
Automatica (Journal of IFAC)
An introduction to Bayesian techniques for sensor networks
WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
A novel system for robust lane detection and tracking
Signal Processing
Relative camera localisation in non-overlapping camera networks using multiple trajectories
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Signal segmentation using changing regression models with application in seismic engineering
Digital Signal Processing
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The Metropolis algorithm has been the most successful and influential of all the members of the computational species that used to be called the "Monte Carlo Method." The Metropolis algorithm began as a technique for attacking specific problems in numerical simulations of physical systems, and interest in it grew slowly at first. But later, the subject exploded as the scope of applications broadened in many surprising directions, including function minimization, computational geometry, and combinatorial counting. Today, topics related to the Metropolis algorithm constitute an entire field of computational science supported by a deep theory and having applications ranging from physical simulations to the foundations of computational complexity