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)
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..