JSM 2015 Online Program

Online Program Home
My Program

Abstract Details

Activity Number: 190
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #316652
Title: Bayesian Network Structure Learning: A Three-Stage Approach and Its Application
Author(s): Kaixian Yu* and Jinfeng Zhang
Companies: Florida State University and Florida State University
Keywords: Bayesian networks ; BNs ; Three-stage

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complicated correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still an open question in BN research. In this study, we present a three-stage approach incorporating sequential importance sampling optimization (SISO). We propose a strategy which requires less observations to accomplish the skeleton discovery, then utilize SISO to find the optimal networks. Eventually we design a new stage to reclaim as many missed edges. We obtained sounding results from testing on simulation networks and the well-known flow cytometry dataset.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home