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QuantTree histograms

QuantTree

3 papers tagged archive 2025-07-28

Introduced by Giacomo Boracchi et al. in QuantTree: Histograms for Change Detection in Multivariate Data Streams

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Given a training set drawn from an unknown d-variate probability distribution, QuantTree constructs a histogram by recursively splitting ℝᵈ. The splits are defined by a stochastic process so that each bin contains a certain proportion of the training set. These histograms can be used to define test statistics (e.g., the Pearson statistic) to tell whether a batch of data is drawn from ϕ₀ or not. The most crucial property of QuantTree is that the distribution of any statistic based on QuantTree histograms is independent of ϕ₀, thus enabling nonparametric statistical testing.

PaperSource

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Change Detection3
Change Point Detection1
Drift Detection1

Usage over time archive 2025-07-28

Papers per year tagged with QuantTree: 2018 to 2022, peak 2 2 0 2018: 1 paper 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 0 papers 2021 2022: 2 papers 2022
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Distribution Approximation

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