hi, > I'd like to ask why `BayesianRidge` and `ARDRegression` do not use > marginal log likelihood (MLL) but learned coefficients to check > convergence when fitting. > I know that most iterative algorithms must have some objective > function by which the convergence is checked. > In Bayesian inference, like variational learning, the objective function is > MLL. > Are there any reason not to use MLL?
to be honest I would say no. It's mostly done this way for historical reasons. This code has not changed much since our first sprints. > And also, is the learning algorithm of `BayesianRidge` and > `ARDRegression` a kind of variational learning? > If so, the MLL is ensured to increase upon learning. > However, the MLL of ARDRegression > (http://scikit-learn.org/stable/_images/plot_ard_3.png) did decrease. > Is this a bug or did I misunderstand something? I am not sure but I remember skipping a test due to a similar problem. See test_bayes.py. This code needs some love and it's great if you dig a bit into it. The notations and algorithms should follow Bishop's book. Alex ------------------------------------------------------------------------------ Keep Your Developer Skills Current with LearnDevNow! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-d2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
