Likelihood Methods
Likelihood methods are statistical techniques used to estimate parameters of a probability distribution by maximizing the likelihood function, which measures how probable observed data is given specific parameter values. They are fundamental in inferential statistics, enabling model fitting, hypothesis testing, and uncertainty quantification. Common applications include maximum likelihood estimation (MLE) and likelihood ratio tests.
Developers should learn likelihood methods when working on data-intensive projects involving statistical modeling, machine learning, or data science, as they provide a rigorous framework for parameter estimation and model comparison. They are essential for tasks like building predictive models, conducting A/B testing, or analyzing experimental data in fields such as bioinformatics, finance, and social sciences.