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Stan [28] is a PPL for specifying models, most often Bayesian. Stan samples posterior distributions using HMC – a variant of Markov Chain Monte Carlo (MCMC). HMC boasts a more robust and efficient approach over Gibbs or Metropolis‐Hastings sampling for complex models, while providing insightful diagnostics to assess convergence and mixing. This may explain why Stan is gaining popularity over other Bayesian samplers (such as BUGS [10] and JAGS [11]).

Stan provides a flexible and principled model specification framework. In addition to fully Bayesian inference, Stan computes log densities and Hessians, variational Bayes, expectation propagation, and approximate integration. Stan is available as a command line tool or R/Python interface (RStan and PyStan, respectively).

Stan has the ability to become the de facto Bayesian modeling software. Designed by thought leader Andrew Gelman and a growing, enthusiastic community, Stan possesses much promise. The language architecture promotes cross‐compatibility and extensibility, and the general‐purpose posterior sampler with innovative diagnostics appeals to novice and advanced modelers alike. Further, to our knowledge, Stan is the only general‐purpose Bayesian modeler that scales to thousands of parameters – a boon for big data analytics.