Igor -I hope that all is well! My group recently released a new library for learning sparse graphical models using ideas from compressed sensing and high-dimensional statistics. For Bayesian networks it scales much better than existing approaches. I would very much appreciate if you consider posting this on your blog, I think the readers of Nuit Blanche would be interested.Here is a more formal announcement:Introducing sparsebn - A new R package for learning sparse graphical models from high-dimensional data via sparse regularization. Designed from the ground up to handle:
- Experimental data with interventions
- Mixed observational / experimental data
- High-dimensional data with p much larger than n
- Datasets with thousands of variables
- Continuous and discrete dataThe emphasis of this package is scalability and statistical consistency on high-dimensional datasets. Compared to existing algorithms, sparsebn scales much better and is under active development. We have several features and improvements in the pipeline that will help scale these algorithms even further.-BBryon AragamCMU Machine Learning DepartmentTwitter: @itsrainingdata
Cool ! here is the attendant paper: Learning Large-Scale Bayesian Networks with the sparsebn Package by Bryon Aragam, Jiaying Gu, Qing Zhou
Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets typically have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we develop a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing packages for this task within the R ecosystem, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. The sparsebn package is open-source and available on CRAN.
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