Random sampling is a method of selecting a subset of items from a population such that every element has a known, non-zero probability of being chosen, with selections governed by chance rather than judgement. It is the foundation of statistical inference, allowing properties of a population to be estimated from a representative sample while quantifying uncertainty. In machine learning it underpins data partitioning, stochastic optimisation and Monte Carlo estimation.
Overview
- Random Sampling is situated within the Statistics area of the artificial-intelligence domain.
- It connects a number of established concepts in the knowledge graph, anchoring edges that previously referenced it without a defining page.
Key aspects
- Statistics (partOf)
- Sampling (implements)
- Random Number Generation (uses)
Mechanisms
- Operates through its relationships with Statistics and Probability.
- Provides capabilities consumed by dependent and enabled classes listed under Relationships.
Applications
- Supports Inference
- Supports Cross-Validation
- Supports Cross-Validation
- Supports Simulation