Module sampling

Data sampling utilities for statistical analysis and machine learning

This module provides various sampling strategies including random sampling, stratified sampling, and importance-weighted sampling. These functions are useful for creating representative subsets of datasets, bootstrap sampling, and handling imbalanced data distributions.

Functions

bootstrap_sample

Generate bootstrap samples from indices

importance_sample

Performs importance sampling based on provided weights

multiple_bootstrap_samples

Generate multiple bootstrap samples

random_sample

Performs random sampling with or without replacement

stratified_sample

Performs stratified random sampling