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
Distributional Gradient Boosting Machines
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
Monotone composite quantile regression neural network (MCQRNN) with tensorflow 2.x.
Prometheus summary with quantiles
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
Aioprometheus summary with quantiles
Set of algorithms, used for estimation statistic characteristics on streaming data.
Compute least squares estimates and IVX estimates with pairwise quantile predictive regressions (R package)
A data structure for accurate on-line accumulation of rank-based statistics.
A q-quantile estimator for high-dimensional distributions
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