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
Distributional Gradient Boosting Machines
C++ version of Ted Dunning's merging t-digest
A library to compute histograms on distributed environments, on streaming data
An extension of Py-Boost to probabilistic modelling
Wicked Fast, Accurate Quantiles Using 'T-Digests'
Agnostic (re)implementations (R/SAS/Python/C) of common quantile estimation algorithms.
C++14 port of the DDSketch distributed quantile sketch algorithm
Python Implementation of Graham Cormode and S. Muthukrishnan's Effective Computation of Biased Quantiles over Data Streams in ICDE’05
Prometheus summary with quantiles
B-digest is a Go library for fast and memory-efficient estimation of quantiles with guaranteed relative error and full mergeability
Aioprometheus summary with quantiles
Set of algorithms, used for estimation statistic characteristics on streaming data.
A data structure for accurate on-line accumulation of rank-based statistics.
A q-quantile estimator for high-dimensional distributions
R package for estimation of elliptical extreme quantile regions
Compute least squares estimates and IVX estimates with pairwise quantile predictive regressions (R package)
An open benchmark for real-time analytics benchmark over massive data sets
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