Maximum Likelihood Estimation
Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution by maximizing a likelihood function, based on observed data. It finds the parameter values that make the observed data most probable under the assumed statistical model. MLE is widely used in fields like machine learning, econometrics, and bioinformatics for model fitting and inference.
Developers should learn MLE when working on statistical modeling, machine learning algorithms (e.g., logistic regression, Gaussian Mixture Models), or data analysis tasks that require parameter estimation from data. It is essential for building and training probabilistic models, performing hypothesis testing, and optimizing models in applications such as natural language processing, finance, and scientific research.