Stan

Probabilistic programming with full Bayesian inference and Hamiltonian Monte Carlo sampling.

EstablishedOpen SourceLow lock-in
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Pricing

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Flat rate

Adoption

→Stable

License

Open Source

Data freshness

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Overview

What is Stan?

Stan is a probabilistic programming language that allows users to implement statistical models using full Bayesian inference. It uses Hamiltonian Monte Carlo for efficient sampling, making it suitable for complex data analysis tasks.

Key differentiator

“Stan stands out for its robust implementation of Bayesian inference and Hamiltonian Monte Carlo, offering unparalleled flexibility and efficiency in statistical modeling.”

Capability profile

Strength Radar

Full Bayesian in…Support for a wi…Highly optimized…Extensive docume…

Honest assessment

Strengths & Weaknesses

↑ Strengths

Full Bayesian inference with Hamiltonian Monte Carlo sampling

Support for a wide range of statistical models

Highly optimized and efficient computation

Extensive documentation and community support

Fit analysis

Who is it for?

✓ Best for

Research teams needing precise Bayesian inference for complex models

Academics working on advanced statistical methods and simulations

Developers building custom statistical tools requiring high performance

✕ Not a fit for

Projects with strict real-time requirements due to computational intensity

Teams preferring a graphical user interface over programming-based solutions

Cost structure

Pricing

Free Tier

None

Starts at

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Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Next step

Get Started with Stan

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →