Stan is a popular open-source probabilistic programming language that allows users to build Bayesian statistical models. It offers a range of features such as advanced algorithms, automatic differentiation, and optimization techniques. While Stan has been a reliable choice for many data scientists and researchers, businesses are constantly looking for alternatives and competitors that offer unique features and better suit their specific needs. In this article, we will explore the 10 best Stan alternatives and competitors in 2024.

1. PyMC3

PyMC3 is a powerful probabilistic programming language built on top of Python, providing a range of features for building Bayesian models. It offers advanced sampling algorithms, automatic differentiation, and model checking functionalities. PyMC3 excels in its ease of use and flexible syntax, making it an excellent alternative to Stan.

2. TensorFlow Probability

TensorFlow Probability is a probabilistic programming language that is built on top of TensorFlow, a popular machine learning framework. It provides a range of features for Bayesian modeling, such as Markov Chain Monte Carlo (MCMC) algorithms, variational inference, and Hamiltonian Monte Carlo (HMC) sampling. TensorFlow Probability stands out for its integration with TensorFlow, making it a powerful tool for data scientists and machine learning experts.

Reading more:

3. Edward2

Edward2 is a probabilistic programming library built on top of TensorFlow, allowing users to easily build Bayesian models. It provides a range of features such as HMC and variational inference, and automatic differentiation. Edward2 stands out for its flexibility, enabling users to define custom probability distributions and build complex models.

4. Pyro

Pyro is a probabilistic programming language built on top of Python, providing a range of features for building Bayesian models. It offers a variety of inference algorithms, including HMC and variational inference, and automatic differentiation. Pyro distinguishes itself by offering an extensive set of pre-built models, making it easy to get started with Bayesian modeling.

5. JAGS

JAGS (Just Another Gibbs Sampler) is a popular probabilistic programming language that allows users to build Bayesian models using Markov Chain Monte Carlo algorithms. It offers a range of features such as likelihood functions, priors, and regression models. JAGS excels in its simplicity and ease of use, making it an excellent alternative to Stan for those new to Bayesian modeling.

6. BUGS

BUGS (Bayesian inference Using Gibbs Sampling) is a probabilistic programming language that allows users to build Bayesian models using Markov Chain Monte Carlo algorithms. It offers a range of features such as hierarchical models, multivariate models, and mixture models. BUGS distinguishes itself by providing a user-friendly interface and a range of graphical output options.

Reading more:

7. emcee

emcee (pronounced "MCMC") is a Python library that provides Markov Chain Monte Carlo sampling for Bayesian modeling. It offers a range of features such as parallel sampling, adaptive proposal distributions, and model comparison. emcee stands out for its speed and efficiency, making it an excellent choice for large datasets.

8. Nimble

Nimble is a probabilistic programming language built on top of R, providing a range of features for building Bayesian models. It offers a variety of sampling algorithms, including HMC and MCMC, and automatic differentiation. Nimble distinguishes itself by offering a flexible syntax and an extensive set of pre-built models.

9. STANza

STANza is a probabilistic programming language built on top of Python, providing a range of features for building Bayesian models. It offers a variety of sampling algorithms, including HMC and NUTS, and automatic differentiation. STANza stands out for its focus on speed and scalability, making it an excellent choice for large datasets.

10. TFP

TFP (TensorFlow Probability) is a probabilistic programming language built on top of TensorFlow, providing a range of features for building Bayesian models. It offers a variety of sampling algorithms, including HMC and variational inference, and automatic differentiation. TFP distinguishes itself by providing an extensive set of pre-built distributions and models, making it easy to get started with Bayesian modeling.

Reading more:

In conclusion, while Stan has been a popular choice for building Bayesian models, there are several alternatives and competitors in 2024 that offer unique features and cater to specific needs. Whether it's PyMC3 with its flexible syntax, TensorFlow Probability with its integration with TensorFlow, or JAGS with its simplicity and ease of use, these 10 Stan alternatives provide data scientists and researchers with a range of options to build Bayesian models and extract insights from their data.