Project SAHDE:
Scalable Adaptive Histogram Density Estimation
This programme is partly supported by:
- databricks academic partners program for distributed cloud computing
- research time for this project was party due to:
- 2009-2015 by industrial consulting revenues of Raazesh Sainudiin
- 2015, 2016 by the project CORCON: Correctness by Construction, Seventh Framework Programme of the European Union, Marie Curie Actions-People, International Research Staff Exchange Scheme with counter-part funding by The Royal Society of New Zealand
Project SAHDE is an effort to create a scalable version of the adaptive histogram density estimators implemented in:
- MRS 2.0, a C++ class library for statistical set processing and computer-aided proofs in statistics.
based on mathematical statistical notions in:
- Data-adaptive histograms through statistical regular pavings, Raazesh Sainudiin, Gloria Teng, Jennifer Harlow and Warwick Tucker, 2016 (PDF 1.8MB)
LICENSE
The license for mrs2 is GNU General Public License (GPL) and that for SAHDE Project is Apache 2.0.
Current Sub-Projects of SAHDE
- SparkDensityTree for scalable density estimation using optimally smoothed L2-risk minimizing penalties (in progress)
- SparkOnlineLearning has potential for streaming regularly paved tree arithmetic by extending from the Scala trees in SparkDensityTree.
Blackboard discussion notes at LaMaStEx on 2016-10-08.
We will eventually lua/la/ka-tex mathematically here..
