Psample

Psample is a term used in computing to denote a sampling operation or set of tools designed to generate samples from data, distributions, or streams. There is no single canonical implementation; several projects and libraries adopt the name or concept, often with domain-specific features.

In probabilistic programming and machine learning, a psample interface typically provides functions to draw realizations from probability distributions, often enabling vectorized batching, broadcasting of shapes, and tracking of log-probabilities. Such tools support common distributions, reparameterization tricks for differentiable sampling, and integration with automatic differentiation systems. They are used to perform Monte Carlo estimates, variational inference, and stochastic optimization, and commonly allow control over random seeds for reproducibility and facilities for caching or replay.

In data processing and experiments, psample can refer to sub-sampling the dataset to reduce computation, using strategies such as uniform random sampling, stratified sampling, or systematic sampling. In benchmarking contexts, psampling helps estimate performance or model behavior with smaller workloads while preserving representativeness.

Design considerations include correctness of sampling method, reproducibility, performance, memory usage, and compatibility with streaming data. Implementations may support lazy evaluation, incremental or online sampling, and integration with data pipelines or deep learning frameworks.

See also: Sampling (statistics), Monte Carlo method, Random number generator.