Likelihood Inference

Likelihood inference is a statistical method used to estimate parameters of a probability distribution based on observed data, by maximizing the likelihood function. It provides a framework for making inferences about unknown parameters, such as point estimates, confidence intervals, and hypothesis tests, without relying on prior distributions. This approach is fundamental in frequentist statistics and is widely applied in fields like econometrics, biostatistics, and machine learning.

Also known as: Maximum Likelihood Estimation, MLE, Likelihood Methods, Frequentist Inference, Statistical Inference

Why learn Likelihood Inference?

Developers should learn likelihood inference when working on data analysis, statistical modeling, or machine learning projects that require parameter estimation from data, such as in regression models, time-series analysis, or probabilistic programming. It is essential for tasks like model fitting, A/B testing, or building predictive algorithms where understanding data uncertainty is critical. For example, in software for financial forecasting or healthcare analytics, likelihood methods help optimize models and assess reliability.

Learning Resources

• tutorial: Introduction to Statistical Inference
• docs: Maximum Likelihood Estimation - Wikipedia
• course: Statistical Inference Course on Coursera
• tutorial: Likelihood Inference in R - DataCamp
• book: All of Statistics: A Concise Course in Statistical Inference