Probabilistic Data Structures and Algorithms in Python
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Updated
Feb 24, 2020 - Python
Probabilistic Data Structures and Algorithms in Python
DynaHist: A Dynamic Histogram Library for Java
C++ version of Ted Dunning's merging t-digest
Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost
Wicked Fast, Accurate Quantiles Using 'T-Digests'
Agnostic (re)implementations (R/SAS/Python/C) of common quantile estimation algorithms.
A data structure for accurate on-line accumulation of rank-based statistics.
A library to compute histograms on distributed environments, on streaming data
Distributional Gradient Boosting Machines
Python Implementation of Graham Cormode and S. Muthukrishnan's Effective Computation of Biased Quantiles over Data Streams in ICDE’05
B-digest is a Go library for fast and memory-efficient estimation of quantiles with guaranteed relative error and full mergeability
Monotone composite quantile regression neural network (MCQRNN) with tensorflow 2.x.
Compute least squares estimates and IVX estimates with pairwise quantile predictive regressions (R package)
A q-quantile estimator for high-dimensional distributions
An open benchmark for real-time analytics benchmark over massive data sets
Aioprometheus summary with quantiles
An implementation of the Greenwald-Khanna approximate quantile streaming algorithm as a Spark user-defined aggregate function.
Deep Quantile Regression Synthetic Aperture Radar Ship Size
Benchmark of different quantile implementations
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