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搜索结果: 1-11 共查到Metropolis algorithm相关记录11条 . 查询时间(0.091 秒)
The Metropolis algorithm is a widely used procedure for sampling from a specified distribution on a large finite set. We survey what is rigorously known about running times. This includes work from ...
Eigen Analysis for Some Examples of the Metropolis Algorithm.
What Do We Know about the Metropolis Algorithm.
Numerical Results for the Metropolis Algorithm
Importance sampling, particularly sequential and adaptive importance sampling, have emerged as competitive simulation techniques to Markov–chain Monte–Carlo techniques. We compare importance sampling ...
We prove sharp rates of convergence to stationarity for a simple case of the Metropolis algorithm: the placement of a single disc of radius h randomly into the interval [−1 − h, 1 + h], w...
This paper gives geometric tools: comparison, Nash and Sobolev inequalities for pieces of the relevent Markov operators, that give useful bounds on rates of convergence for the Metropolis algorithm. A...
Recently, the idea of classical Metropolis sampling through Markov chains has been generalized for quantum Hamiltonians. However, the underlying Markov chain of this algorithm is still classical in na...
This paper considers the stability and ergodicity of an adaptive random walk Metropolis algorithm. The algorithm adjusts the scale of the symmetric proposal distribution continuously, based on the o...
In this paper we study the Metropolis algorithm in connection with two mean–field spin systems, the so called mean–field Ising model and the BlumeEmery–Griffiths model. In both this examples the na...
Surface measurement of complex geometric objects by means of optical measurement techniques, such as laser scanning or photogrammetry, requires multiple scans from various stations. The recorded dat...

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