📌 Expectation-Maximisation Algorithm Summary

The Expectation-Maximisation (EM) Algorithm is a method used to find the most likely parameters for statistical models when some data is missing or hidden. It works by alternating between estimating missing data based on current guesses and then updating those guesses to better fit the observed data. This process repeats until the solution stabilises and further changes are minimal.

🙋🏻‍♂️ Explain Expectation-Maximisation Algorithm Simply

Imagine you are trying to solve a puzzle, but some pieces are missing. First, you make your best guess about what the missing pieces might look like. Then, you use those guesses to improve your overall picture. By repeating this process, your guesses get better each time, even though you never see the missing pieces directly.

📅 How Can it be used?

The Expectation-Maximisation Algorithm can be used to group customers with similar behaviours when some purchase data is incomplete.

🗺️ Real World Examples

In healthcare, the EM Algorithm helps researchers estimate how many people have a certain disease when some patients do not report their symptoms. By iteratively filling in the missing information and updating the estimates, the algorithm provides a clearer picture of disease prevalence.

In speech recognition, the EM Algorithm is used to train models that convert spoken words into text, even when some sounds are unclear or missing in the recordings. It helps the system learn patterns despite incomplete data.

✅ FAQ

The Expectation-Maximisation Algorithm helps find the most likely settings for a statistical model when some information is missing or hidden. It is especially handy in situations where you have incomplete data but still want to make the best possible predictions or decisions based on what you do know.

It works a bit like filling in the blanks. First, it takes a guess at the missing parts, then it updates its guesses to better fit the data you can see. This back-and-forth continues until the changes get very small and the solution settles down.

You might find it in action behind the scenes in things like speech recognition, image processing, and genetics research. Anywhere there is incomplete information but a need to make sense of what is available, this algorithm can be quite useful.

📚 Categories

• Artificial Intelligence
• Data Science
• Model Training & Tuning

🔗 External Reference Links

Expectation-Maximisation Algorithm link

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