AI engineering glossary · Probability
What is importance sampling?
Importance sampling: A Monte Carlo technique that estimates an expectation under one distribution by sampling from another easier one and reweighting each sample by the ratio of the two densities. It helps with rare events and is used in off-policy reinforcement learning.
Importance sampling: an example
To estimate the tiny chance of a very large loss, sample from a distribution that produces large losses more often and correct with weights.
Related terms
More Probability terms
- Independence
- Intersection of events
- Inverse transform sampling
- Joint distribution
- Joint probability
- KL divergence
- Law of large numbers
- Law of total expectation
Where you learn it
Importance sampling is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.