What are some common methods of making distribution predictions? I have a set of features $x_1,x_2,x_3$ which map to Gaussian distributions ($\mu,\sigma^2$). That is, the feature vector of a single example $X_1$ can be defined by a distribution with ($\mu_1,\sigma^2_1$).
What are some ways to go about building a model which can predict ($\mu,\sigma^2$) given a feature vector $X$? I think my biggest issue here is that I don't know which terms to search for, so I'm having trouble finding relevant papers or software packages. Recommended R libraries would be a bonus!
