2 research outputs found
Learning Chordal Markov Networks via Branch and Bound
We present a new algorithmic approach for the task of finding a chordal Markov network structure that maximizes a given scoring function. The algorithm is based on branch and bound and integrates dynamic programming for both domain pruning and for obtaining strong bounds for search-space pruning. Empirically, we show that the approach dominates in terms of running times a recent integer programming approach (and thereby also a recent constraint optimization approach) for the problem. Furthermore, our algorithm scales at times further with respect to the number of variables than a state-of-the-art dynamic programming algorithm for the problem, with the potential of reaching 20 variables and at the same time circumventing the tight exponential lower bounds on memory consumption of the pure dynamic programming approach.Peer reviewe
Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models
Describing the relationship between the variables in a study domain and
modelling the data generating mechanism is a fundamental problem in many
empirical sciences. Probabilistic graphical models are one common approach to
tackle the problem. Learning the graphical structure is computationally
challenging and a fervent area of current research with a plethora of
algorithms being developed. To facilitate the benchmarking of different
methods, we present a novel automated workflow, called benchpress for producing
scalable, reproducible, and platform-independent benchmarks of structure
learning algorithms for probabilistic graphical models. Benchpress is
interfaced via a simple JSON-file, which makes it accessible for all users,
while the code is designed in a fully modular fashion to enable researchers to
contribute additional methodologies. Benchpress currently provides an interface
to a large number of state-of-the-art algorithms from libraries such as BiDAG,
bnlearn, GOBNILP, pcalg, r.blip, scikit-learn, TETRAD, and trilearn as well as
a variety of methods for data generating models and performance evaluation.
Alongside user-defined models and randomly generated datasets, the software
tool also includes a number of standard datasets and graphical models from the
literature, which may be included in a benchmarking workflow. We demonstrate
the applicability of this workflow for learning Bayesian networks in four
typical data scenarios. The source code and documentation is publicly available
from http://github.com/felixleopoldo/benchpress.Comment: 30 pages, 1 figur