In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is …

在 统计学 中， 最大似然估计 （英語： maximum likelihood estimation，簡作 MLE），也称 极大似然估计，是用来 估計 一个 概率模型 的参数的一种方法。 下方的讨论要求读者熟悉 概率论 中的基本定义，如 概率分布 、 概率密度函数 、 随机变量 、 数学期望 等。 读者還須先熟悉 连续 实函数 的基本性质，比如使用 微分 来求一个函数的 极值 （即 极大值 或 极小值）。 同時，讀者須先擁有 似然函數 的背景知識，以了解最大似然估計的出發點及應用目的。 给定一个概率分布 ， …

本文将聚焦于targeted learning的核心方法， Targeted Maximum Likelihood Estimation，即 TMLE （也可以一般化称为Targeted Minimum-Loss-based Estimation，再通俗点Targeted Machine-Learning-based Estimation或许也说得通）。 从方法论上讲，TMLE是在我们的roadmap框架下前面文章中Super Learning的一个增强版；而从统计理论角度上，TMLE是 半参数方法 (semiparametric)，使我们在比较弱的假定下获得相合的 (consistent)参数估计，在稍强的假定 …

2024年9月26日 · Probability Density and Maximum Likelihood Estimation (MLE) are essential tools for effectively analyzing and interpreting continuous data. The Probability Density Function (PDF) offers a clear visualization of how data points are distributed, while maximum likelihood estimation provides a robust method for estimating the parameters that best ...

Maximum likelihood estimation (MLE) of the parameters of the normal distribution. Derivation and properties, with detailed proofs.

Maximum Likelihood Estimation (MLE) is a widely used statistical estimation method. In this lecture, we will study its properties: efficiency, consistency and asymptotic normality. MLE is a method for estimating parameters of a statistical model.

2013年8月18日 · Maximum-Likelihood Estimation (MLE) is a statistical technique for estimating model parameters. It basically sets out to answer the question: what model parameters are most likely to characterise a given set of data? First you need to select a model for the data. And the model must have one or more (unknown) parameters.

It is known for its signature bell curve shape, where the likelihood of a value being close to the mean is much higher than the likelihood of a value being far from the mean. The Gaussian distribution is often denoted by N(μ, σ2).

MLE of the Poisson parameter, N %&’, is the unbiased estimate of the mean, $J(sample mean)

2013年8月18日 · Maximum-Likelihood Estimation (MLE) is a statistical technique for estimating model parameters. It basically sets out to answer the question: what model parameters are most likely to characterise a given set of data? First you need to select a model for the data. And the model must have one or more (unknown) parameters.