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On the Geometric Ergodicity of Metropolis-Hastings Algorithms for Lattice Gaussian Sampling
Lattice Gaussian sampling MCMC methods Metropolis-Hastings algorithm
2019/6/10
Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm in Markov chain ...
A GEOMETRIC INTERPRETATION OF THE METROPOLIS-HASTINGS ALGORITHM
Algorithm geometry explained
2015/7/14
A GEOMETRIC INTERPRETATION OF THE METROPOLIS-HASTINGS ALGORITHM.
Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals
Ergodic convergence Markov Chain Monte Carlo Metropolis-within Gibbs composite sampling Multivariatet mixtures Simulated annealing Variational Approx-imation
2013/6/14
This article develops a general-purpose adaptive sampler that approximates the target density by a mixture of multivariate t densities. The adaptive sampler is based on reversible proposal distributio...
针对目标运动模型不完全的跟踪系统，为解决系统误差配准问题，提出一种基于Metropolis-Hastings抽样的系统误差配准方法。该方法通过系统误差的最大似然估计导出的等效概率平稳函数作为Metropolis-Hastings算法要求构造的概率密度函数，同时给出不同的提议函数来提高系统误差空间分布的全局性。对时变和时不变系统误差情况分别进行了仿真，仿真结果表明,所提方法在考虑系统误差统计特性的同...
针对目标运动模型不完全的跟踪系统，为解决系统误差配准问题，提出一种基于Metropolis-Hastings抽样的系统误差配准方法。该方法通过系统误差的最大似然估计导出的等效概率平稳函数作为Metropolis-Hastings算法要求构造的概率密度函数，同时给出不同的提议函数来提高系统误差空间分布的全局性。对时变和时不变系统误差情况分别进行了仿真，仿真结果表明,所提方法在考虑系统误差统计特性的同...
Exact recording of Metropolis-Hastings-class Monte Carlo simulations using one bit per sample
Markov chain Monte Carlo Metropolis-Hastings information theory data representation
2011/6/21
The Metropolis-Hastings (MH) algorithm is the prototype for a class of Markov chain Monte Carlo methods
that propose transitions between states and then accept or reject the proposal. These methods g...
Using parallel computation to improve Independent Metropolis--Hastings based estimation
MCMC algorithm independent Metropolis{Hastings
2010/10/19
In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent. In particular, we p...
针对短采样宽带信号近似最大似然（approximated maximum likelihood,AML）方位估计计算量大的问题，将马尔科夫链〖CD*2〗蒙特卡罗方法与近似最大似然方位估计相结合，提出一种基于MetropolisHastings抽样的近似最大似然方位估计方法（AMLMH）。该方法将AML算法的空间谱函数作为信号的概率分布函数，并利用MetropolisHastings抽样方法...
Variable-at-a-time Implementations of Metropolis-Hastings
Variable-at-a-time Implementations Metropolis-Hastings
2010/3/18
It is common practice in Markov chain Monte Carlo to update a high-dimensional
chain one variable (or sub-block of variables) at a time, rather than conduct a single block
update. While this modific...