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-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Metropolis-Hastings抽样的近似最大似然方位估计方法(AMLMH)。该...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-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Metropolis-Hastings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Metropolis-Hastings抽样方法从该概率分布函数中抽样。研究结果表明,AMLMH方法不但保持了原近似最大似然方位估计方法的优良性能,而且减小了计算量。展开更多
A fast MUltiple SIgnal Classification (MUSIC) spectrum peak search algorithm is devised, which regards the power of the MUSIC spectrum function as target distribution up to a constant of proportionality, and uses Metr...A fast MUltiple SIgnal Classification (MUSIC) spectrum peak search algorithm is devised, which regards the power of the MUSIC spectrum function as target distribution up to a constant of proportionality, and uses Metropolis-Hastings (MH) sampler, one of the most popular Markov Chain Monte Carlo (MCMC) techniques, to sample from it. The proposed method reduces greatly the tremendous computation and storage costs in conventional MUSIC techniques i.e., about two and four orders of magnitude in computation and storage costs under the conditions of the experiment in the paper respectively.展开更多
This paper mainly talks about a popular approach of volatility of a GARCH-type model in R, while the disturbances are independent and have identical Student-t distribution. It uses the Metropolis-Hastings method to pe...This paper mainly talks about a popular approach of volatility of a GARCH-type model in R, while the disturbances are independent and have identical Student-t distribution. It uses the Metropolis-Hastings method to perform the computations and gives the programs in details in R.展开更多
基于马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法的α稳定分布参数估计具有良好的性能,但不合适的提议函数常导致算法不收敛或混合性能不好。针对提议函数难以选择的问题,提出了一种基于自适应Metropolis算法的非对称α稳...基于马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法的α稳定分布参数估计具有良好的性能,但不合适的提议函数常导致算法不收敛或混合性能不好。针对提议函数难以选择的问题,提出了一种基于自适应Metropolis算法的非对称α稳定分布参数估计新方法。该方法利用Markov链的历史信息自动调整提议函数的协方差矩阵,使其不断地逼近目标分布,从而获得更好的估计结果。理论分析和仿真结果表明,此方法不仅能准确地估计出α稳定分布的4个参数,而且具有良好的鲁棒性和灵活性。展开更多
文摘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-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Metropolis-Hastings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Metropolis-Hastings抽样方法从该概率分布函数中抽样。研究结果表明,AMLMH方法不但保持了原近似最大似然方位估计方法的优良性能,而且减小了计算量。
基金Supported by the National Natural Science Foundation of China (No.60172028).
文摘A fast MUltiple SIgnal Classification (MUSIC) spectrum peak search algorithm is devised, which regards the power of the MUSIC spectrum function as target distribution up to a constant of proportionality, and uses Metropolis-Hastings (MH) sampler, one of the most popular Markov Chain Monte Carlo (MCMC) techniques, to sample from it. The proposed method reduces greatly the tremendous computation and storage costs in conventional MUSIC techniques i.e., about two and four orders of magnitude in computation and storage costs under the conditions of the experiment in the paper respectively.
文摘This paper mainly talks about a popular approach of volatility of a GARCH-type model in R, while the disturbances are independent and have identical Student-t distribution. It uses the Metropolis-Hastings method to perform the computations and gives the programs in details in R.
文摘基于马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法的α稳定分布参数估计具有良好的性能,但不合适的提议函数常导致算法不收敛或混合性能不好。针对提议函数难以选择的问题,提出了一种基于自适应Metropolis算法的非对称α稳定分布参数估计新方法。该方法利用Markov链的历史信息自动调整提议函数的协方差矩阵,使其不断地逼近目标分布,从而获得更好的估计结果。理论分析和仿真结果表明,此方法不仅能准确地估计出α稳定分布的4个参数,而且具有良好的鲁棒性和灵活性。
