estimam

Estimam is a fictional software library used in educational materials to illustrate estimation methods in statistics and machine learning. It does not refer to a real project, but is presented here to demonstrate how a lightweight estimation framework might be described in a wiki-style article. The name stems from the Latin root aestim-, meaning to value or judge, reflecting its focus on estimation.

Overview

Estimam provides a minimal API to define a probabilistic model, specify a likelihood function, and compute estimators using both frequentist and Bayesian approaches. It is designed to be approachable for teaching, while remaining flexible enough to handle common toy problems as well as more realistic models. The library emphasizes transparency, reproducibility, and simplicity, with clear default behaviors and straightforward error reporting.

Design and components

The framework typically includes a model specification layer, an estimator engine, and a set of backends for optimization and sampling. It supports analytic solutions for simple cases and numerical methods for more complex ones, including gradient-based optimization and basic Markov chain Monte Carlo sampling. Estimators can be compared through standard metrics, and results are produced with traceability suitable for instructional demonstrations.

History and usage

Estimam appears in educational contexts as a toy platform to illustrate concepts such as maximum likelihood estimation, Bayesian posterior inference, and model comparison. It is used in classroom demonstrations, problem sets, and introductory course materials to help learners compare estimation techniques in a controlled, transparent setting.

See also

Estimation theory; Maximum likelihood estimation; Bayesian statistics.