A closed-form approach to Bayesian inference in tree-structured graphical models

File Description SizeFormat 
1504.02723v4.pdfWorking paper2 MBAdobe PDFView/Open
Title: A closed-form approach to Bayesian inference in tree-structured graphical models
Authors: Schwaller, L
Robin, S
Stumpf, M
Item Type: Working Paper
Abstract: We consider the inference of the structure of an undirected graphical model in an exact Bayesian framework. More specifically we aim at achieving the inference with close-form posteriors, avoiding any sampling step. This task would be intractable without any restriction on the considered graphs, so we limit our exploration to mixtures of spanning trees. We consider the inference of the structure of an undirected graphical model in a Bayesian framework. To avoid convergence issues and highly demanding Monte Carlo sampling, we focus on exact inference. More specifically we aim at achieving the inference with close-form posteriors, avoiding any sampling step. To this aim, we restrict the set of considered graphs to mixtures of spanning trees. We investigate under which conditions on the priors - on both tree structures and parameters - exact Bayesian inference can be achieved. Under these conditions, we derive a fast an exact algorithm to compute the posterior probability for an edge to belong to {the tree model} using an algebraic result called the Matrix-Tree theorem. We show that the assumption we have made does not prevent our approach to perform well on synthetic and flow cytometry data.
URI: http://hdl.handle.net/10044/1/62721
Copyright Statement: © 2017 The Author(s).
Keywords: stat.ML
Appears in Collections:Faculty of Natural Sciences



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons