Library for concentration bounds

Project description

Concentration bounds library

concentration_bounds: marginal (non-uniform) bounds for bounded (Hoeffding, Bernstein, Bentkus...) and unbounded (sub-Gaussian Chernoff...) distributions.
concentration_variance_bounds: marginal (non-uniform) bounds on the standard deviation process for bounded distributions.
empirical_concentration_bounds: marginal bounds with data-dependent estimators rather than fixed parameters (e.g the variance in Bernstein bound is estimated from the data instead of being prior knowledge).
uniform_concentration_bounds: sample size-uniform bounds (union, time-peeling, method of mixtures...).

0.2.0

Jun 14, 2023

0.1.2

Nov 15, 2021

0.1.1

Nov 10, 2021

This version

0.1.0

Nov 10, 2021

0.0.1

Jul 20, 2021

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Uploaded Nov 10, 2021 Source

Uploaded Nov 10, 2021 Python 3

Hashes for concentration_lib-0.1.0.tar.gz
Algorithm	Hash digest
SHA256	`11a776c9f9cd81a9ee7e58b578de2fde24a8ffb512476de98be94bae7e1afad6`
MD5	`096902be6f8412fef1f1830956c42c6c`
BLAKE2b-256	`76e91db76f7b599b9ca4ca65482e59acea42bc1d2bfd90974e0ffa5924e1d1e3`

Hashes for concentration_lib-0.1.0-py3-none-any.whl
Algorithm	Hash digest
SHA256	`2c60360221f90bc932069ecbff421accb6f4aa6069e50e51a5c7451e33544d08`
MD5	`d964fad7095c8437474ef4ce50eecb27`
BLAKE2b-256	`6bdf7d1ef83880db531d7d36b10cb96bc58081b2750ffd4f84ce00c8cae5f884`