Conditional Distributions

Conditional distributions describe how one random variable behaves given another is fixed at a specific value. They represent P(X|Y=y), the distribution of X when we know Y equals y.

Machine learning algorithms frequently use conditional distributions to make predictions. For example, classification models estimate P(Class|Features), while conditional generative models learn P(Image|Label) or P(Text|Context). These conditional distributions enable the model to generate appropriate outputs for specific inputs or conditions.