Benchmarking of dynamic Bayesian networks inferred from stochastic time-series data.

Abstract

We seek to quantify the failure and success of dynamic Bayesian networks (DBNs), a popular tool for reverse-engineering networks from time-series data. In particular, we focus on data generated by continuous time processes (e.g., genetic expression) and sampled at discrete times. To facilitate analysis and interpretation, we employ a "minimal model" to… (More)

Topics

Figures and Tables

Sorry, we couldn't extract any figures or tables for this paper.

Slides referencing similar topics