后验分布的英文
posterior distribution
例句与用法
更多例句： 下一页- However , for non - linear models and non - gaussian noise , such closed form expressions are almost impossible to obtain , and sequtial monte carlo method provides its approximation
这种方法的基本思想是产生服从后验分布的样本，并对其进行加权，以得到后验密度函数的近似解。 - From a bayesian viewpoint , a growth curve model is studied . with a conjugate prior , the posterior distribution of and that of are given respectively
摘要从贝叶斯观点利用共轭先验考查了增长曲线模型。得到了参数和协方差的边缘后验分布，并在此基础上给出的后验估计、估计域和的后验估计。 - It uses particles to describe the state space . the discretely random measure composed by particles and associated weights approximates to the true posterior state distribution , and is updated by iteration of the algorithm
它采用粒子描述状态空间,用由粒子及其权重组成的离散随机测度近似真实的状态后验分布,并且根据算法递推更新离散随机测度。 - Markov chain monte carlo simulation ( mcmc ) was taken to sample the posterior distribution to get the marginal posterior probability function of the parameters , and the statistical quantities such as the mathematic expectation were calculated
通过马尔科夫链蒙特卡罗模拟对后验分布进行了采样，获得了参数的后验边缘概率密度，并在此基础上获得了参数的数学期望等统计量。 - Here we developed the general arma ( p , q ) - garch ( r , s ) - m ( k ) models , which maybe become increasingly important for estimating volatility returns and exogenous shocks for finance data . after we present the posterior distribution of the model and the full conditional distributions of all the parameters of the model , we develop a hybrid metropolis - hastings algorithm for estimating the parameters of arma - garch - m models based on the works of bayesian chib and greenberg ( 1994 ) and nakatsuma ( 2000 ) . here we simplified the estimations in ma and garch block
作为该模型的推广，我们在本文中提出了一个一般的arma ( p , q ) - garch ( r , s ) - m ( k )模型，并在详细给出模型的后验分布以及模型的所有参数的满条件分布的基础上，结合chibandgreenberg ( 1994 )与nakatsuma ( 2000 )等人的工作，对此新模型设计了一个可行的混合metropolis - hastings算法，简化了ma块与garch块的估计。
