Maximum Likelihood Estimation vs Bayesian Estimation

Developers should learn MLE when working on statistical modeling, machine learning algorithms (e meets developers should learn bayesian estimation when working on projects involving uncertainty quantification, such as a/b testing, recommendation systems, or predictive modeling in data science and machine learning. Here's our take.

Maximum Likelihood Estimation

Nice Pick

Developers should learn MLE when working on statistical modeling, machine learning algorithms (e

Pros

• g
• Related to: statistical-inference, parameter-estimation

Cons

• Specific tradeoffs depend on your use case

Bayesian Estimation

Developers should learn Bayesian estimation when working on projects involving uncertainty quantification, such as A/B testing, recommendation systems, or predictive modeling in data science and machine learning

Pros

• It is particularly useful in scenarios where prior information is available (e
• Related to: bayesian-networks, markov-chain-monte-carlo

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Maximum Likelihood Estimation if: You want g and can live with specific tradeoffs depend on your use case.

Use Bayesian Estimation if: You prioritize it is particularly useful in scenarios where prior information is available (e over what Maximum Likelihood Estimation offers.

The Bottom Line

Maximum Likelihood Estimation wins

Developers should learn MLE when working on statistical modeling, machine learning algorithms (e