Maximum Likelihood Estimation 既然是maximum likelihood,,那么当然首先要写出likelihood,确切的说这个是marginal likelihood, p(\bm{y} | X, \theta) = \int p(\bm{y} | \bm{f}, X, \theta) p(\bm{f} | X, \theta) \mathrm{d} \bm{f}。 在GPR模型中,我们知道\bm{f} \sim \mathcal{GP}(...
因此,假设数据服从高斯分布,我们要计算新的样本x属于训练数据样本分布的概率,那么我们就需要求得样本分布的均值μ和方差 。 三、maximum likelihood 极大似然估计 整个高斯分布出现的几率=使用这个高斯分布产生样本x1-xn的几率,上式n=79 也就是说,我们在求μ*和 *的过程,就是找到一个分布,使得该分布找到样本x的...
2) maximum likelihood classification 最大似然分类法 1. Then based on the enhancement of extraction withmaximum likelihood classificationand compared the result with the effect that usedmaximum likelihood classificationonly. 以陕北农牧交错带为试验区,采用主成分分析法对TM图像进行增强,然后应用最大似然分类法...
Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification 来自 Semantic Scholar 喜欢 0 阅读量: 46 作者:SD Zenzo,R Bernstein,Stephen D. DeGloria,HG Kolsky 摘要: In this paper we present the results of a study of performance of a previously proposed ...
Maximum Likelihood - Mixture of Gaussians Bernt Schiele, ETH Zurich April 25, 2002 In the following I derive the standard equation for the Maximum Likelihood estimation for a mixture of Gaussians. I will concentrate on the mean of a single Gaussian. The other estimates (for the variance and ...
Caglar Ar,Selim Aksoy,Orhan Arikan."Maximum likelihood estimation of Gaussian mixture models using stochastic search". Pattern Recognition . 2012Caglar A, Aksoy S, Arikan O (2012) Maximum likelihood esti- mation of Gaussian mixture models using stochastic search. Pattern Recognit 45(7):2804-2816...
In this paper, we address the problem of identifying convolutive channels using a Gaussian maximum-likelihood (ML) approach when short training sequences (possibly shorter than the channel impulse-response length) are periodically inserted in the transmitted signal. We consider the case where the chan...
The parameters are estimated by means of the expectation-maximization algorithm according to the maximum likelihood approach. Under Gaussian assumptions, we analyse the complete-data likelihood function of cluster weighted models. Further, under suitable hypotheses we show that the maximization of the ...
The technique, called hereafter mixture of maximum-likelihood normalized projections (mixture of ML-NP), was used in this work to classify a 44-dimensional data set into 4 classes (bag, trolley, single person, group of people). The accuracy achieved on an independent test set is 98% vs. ...
Since we are able to write the Gaussian mixture model as a latent-variable model, we can use theEM algorithmto find the maximum likelihood estimators of its parameters. Starting from an initial guess of the parameter vector , the algorithm produces a new estimate of the parameter vector ...