EM Algorithm Explained in One Picture
The EM algorithm finds maximum-likelihood estimates for model parameters when you have incomplete data. The "E-Step" finds probabilities for the assignment of data points, based on a set of hypothesized probability density functions; The "M-Step" updates the original hypothesis with new data. The cycle repeats until the parameters stabilize.
Click on the picture to zoom in
DSC Resources
- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Deep Analytical Thinking and Data Science Wizardry
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions
Comment
-
Comment by Lance Norskog on March 14, 2019 at 11:55am
-
Thank you for this, it really helped.
Lance Norskog
Resources
Videos
© 2020 Data Science Central ®
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
You need to be a member of Data Science Central to add comments!
Join Data Science Central