Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach

By Jennifer Listgarten, David E. Heckerman. 2007

27 citations recorded by the source corpus.

In many application domains, such as computational biology, the goal of graphical model structure learning is to uncover discrete relationships between entities. For example, in our problem of interest concerning HIV vaccine design, we want to infer which HIV peptides interact with which immune system molecules (HLA molecules). For problems of this nature, we are interested in determining the number of non-spurious arcs in a learned graphical model. We describe both a Bayesian and frequentist approach to this problem. In the Bayesian approach, we use the posterior distribution over model structures to compute the expected number of true arcs in a learned model. In the frequentist approach, we develop a method based on the concept of the False Discovery Rate. On synthetic data sets generated from models similar to the ones learned, we find that both the Bayesian and frequentist approaches yield accurate estimates of the number of non-spurious arcs. In addition, we speculate that the frequentist approach, which is non-parametric, may outperform the parametric Bayesian approach in situations where the models learned are less representative of the data. Finally, we apply the frequentist approach to our problem of HIV vaccine design.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sivaraman Balakrishnan
2. Jaime G. Carbonell
3. Su-In Lee
4. Mohit Bansal
5. Przemyslaw Biecek
6. Scott M. Lundberg
7. Noah A. Smith
8. Kiri L. Wagstaff

In Control and Systems Engineering

1. Jaime G. Carbonell
2. Su-In Lee
3. Sivaraman Balakrishnan

In Statistics and Probability

1. Jaime G. Carbonell
2. Su-In Lee
3. Sivaraman Balakrishnan
4. Larry A. Wasserman

Citations in the corpus

• Learning generative models for protein fold families (2011, methodology): “A shows a qualitative analysis of edges missed by each method (we consider all simulated graphs and the best learnt graph of each method).”
• Finding and Leveraging Structure in Learning Problems (2012, methodology): “A more robust method, which we use, is to use the average regularization parameter obtained from multiple random permutations as in the work of Listgarten and Heckerman [128].”