A novel method is proposed for digital modulation classification based on Markov chain Monte Carlo (MCMC).
提出了一种基于马尔可夫链蒙特卡罗(MCMC)的数字调制分类方法。
Accompanying this have been new approaches to data analysis using, for example, Markov Chain Monte Carlo simulations that are hugely computer intensive.
伴随着这个，又有了使用的数据分析新方法，例如，马尔科夫链，蒙特卡洛模拟这些大型计算机密集型算法。
The series are calculated and analyzed by correlation analysis, stochastic simulation, Monte Carlo, and Markov chain, Monte Carlo, so a group model of risk function is established.
采取相关分析、随机模拟、蒙特卡罗和马尔柯夫链等方法进行了一系列的分析和计算，从而建立了一组风险函数。
Given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the Monte Carlo Markov Chain (MCMC) sampling method.
该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。
Given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the Monte Carlo Markov Chain (MCMC) sampling method.
该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。
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