BART: Bayesian Additive Regression Trees
1 Introduction We consider the fundamental problem of making inference about an unknown function f that predicts an output Y using a p dimensional vector of inputs x = (x1;:::;xp) when Y = f(x)+†; † » N(0;¾2): (1) To do this, we consider modelling or at least approximating f(x) = E(Y jx), the mean of Y given x, by a sum of m regression trees f(x) … h(x) · Pm j=1 gj(x)
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