Type-based MCMC for Sampling Tree Fragments from Forests

Sampling Tree Fragments from Forests

Bayesian learning of forest and tree graphical models

In Bayesian learning of Gaussian graphical model structure, it is common to restrict attention to certain classes of graphs and approximate the posterior distribution by repeatedly moving from one graph to another, using…

Exogenous Randomness Empowering Random Forests

We offer theoretical and empirical insights into the impact of exogenous randomness on the effectiveness of random forests with tree-building rules independent of training data. We formally introduce the concept of exoge…

When do random forests fail?

Random forests are learning algorithms that build large collections of random trees and make predictions by averaging the individual tree predictions. In this paper, we consider various tree constructions and examine how…

Parallelising MCMC via Random Forests

For Bayesian computation in big data contexts, the divide-and-conquer MCMC concept splits the whole data set into batches, runs MCMC algorithms separately over each batch to produce samples of parameters, and combines th…