Likelihood Based Inference vs Frequentist Inference

Developers should learn Likelihood Based Inference when working on data science, machine learning, or statistical modeling projects that require robust parameter estimation from data, such as in regression analysis, time series forecasting, or probabilistic programming meets developers should learn frequentist inference when building data-driven applications, conducting a/b testing, or performing statistical analysis in fields like machine learning, data science, and experimental research. Here's our take.

Likelihood Based Inference

Nice Pick

Developers should learn Likelihood Based Inference when working on data science, machine learning, or statistical modeling projects that require robust parameter estimation from data, such as in regression analysis, time series forecasting, or probabilistic programming

Pros

• It is essential for tasks like building predictive models, conducting A/B testing, or implementing algorithms that involve optimization of statistical models, as it provides a principled way to infer parameters and assess model fit
• Related to: statistical-inference, bayesian-inference

Cons

• Specific tradeoffs depend on your use case

Frequentist Inference

Developers should learn frequentist inference when building data-driven applications, conducting A/B testing, or performing statistical analysis in fields like machine learning, data science, and experimental research

Pros

• It is essential for tasks such as validating model performance, determining statistical significance in experiments, and making data-informed decisions in software development, as it provides objective, repeatable methods for inference without subjective prior assumptions
• Related to: statistics, hypothesis-testing

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Likelihood Based Inference if: You want it is essential for tasks like building predictive models, conducting a/b testing, or implementing algorithms that involve optimization of statistical models, as it provides a principled way to infer parameters and assess model fit and can live with specific tradeoffs depend on your use case.

Use Frequentist Inference if: You prioritize it is essential for tasks such as validating model performance, determining statistical significance in experiments, and making data-informed decisions in software development, as it provides objective, repeatable methods for inference without subjective prior assumptions over what Likelihood Based Inference offers.

The Bottom Line

Likelihood Based Inference wins

Developers should learn Likelihood Based Inference when working on data science, machine learning, or statistical modeling projects that require robust parameter estimation from data, such as in regression analysis, time series forecasting, or probabilistic programming