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极大似然估计_百度百科 极大似然估计方法（Maximum Likelihood Estimate，MLE）也称为最大概似估计或最大似然估计，是求估计的另一种方法，最大概似是1821年首先由德国数学家高斯（C F Gauss）提出，但是这个方法通常被归功于英国的统计学家罗纳德·费希尔（R A Fisher）
MLE - 鲸析数据 MLE is extensively applied in statistics and machine learning for estimating parameters in regression models, classification models, and other probabilistic models With a sufficiently large sample size, MLE typically provides accurate parameter estimates
1. 2 - Maximum Likelihood Estimation | STAT 415 It seems reasonable that a good estimate of the unknown parameter θ would be the value of θ that maximizes the probability, errrr that is, the likelihood of getting the data we observed (So, do you see from where the name "maximum likelihood" comes?) So, that is, in a nutshell, the idea behind the method of maximum likelihood estimation But how would we implement the method in