Hi, I have a question regarding the modeling methodology of the following problem:
* I have two data sets {X_i,y_i} {X_i,z_i}, i=1..N, where y_i = f(X_i) + i.i.d. Gaussian noise and z_i = g(X_i) + i.i.d. Gaussian noise * I apply bayesian linear regression to each of them and obtain p(y|X) and p(z|X) I would like to improve the prediction of the two models using the knowledge that f and g are related (for example f(X_i) = g(X_i) - 1). I can obtain a model p(y,z). I know that there are methods for multi-output regression, but I hope both can be modeled independently and then combined. Thank you for your help! Steffan -- ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.