Markov Chain Monte Carlo using Tree-Based Priors on Model Structure [article]

Nicos Angelopoulos, James Cussens
2013 arXiv   pre-print
We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key idea is that structure priors are defined via a probability tree and that the proposal mechanism for the Metropolis-Hastings algorithm operates by traversing this tree, thereby defining a cheaply computable acceptance probability. We have applied this approach to Bayesian net structure learning using a number of priors and tree traversal
more » ... s. Our results show that these must be chosen appropriately for this approach to be successful.
arXiv:1301.2254v1 fatcat:jxtsr4xqwfcrblhr5lzyl6l7ye