Python interface to CmdStan
CmdStanPy is a lightweight interface to Stan for Python users which provides the necessary objects and functions to do Bayesian inference given a probability model written as a Stan program and data. Under the hood, CmdStanPy uses the CmdStan command line interface to compile and run a Stan program.
Clean interface to Stan services so that CmdStanPy can keep up with Stan releases.
Provide access to all CmdStan inference methods.
Easy to install,
- minimal Python library dependencies: numpy, pandas
- Python code doesn't interface directly with c++, only calls compiled executables
Modular - CmdStanPy produces a MCMC sample (or point estimate) from the posterior; other packages do analysis and visualization.
CmdStan's source-code repository is hosted here on GitHub.
The CmdStanPy, CmdStan, and the core Stan C++ code are licensed under new BSD.
import os from cmdstanpy import cmdstan_path, CmdStanModel # specify locations of Stan program file and data bernoulli_stan = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan') bernoulli_data = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.data.json') # instantiate a model; compiles the Stan program by default bernoulli_model = CmdStanModel(stan_file=bernoulli_stan) # obtain a posterior sample from the model conditioned on the data bernoulli_fit = bernoulli_model.sample(chains=4, data=bernoulli_data) # summarize the results (wraps CmdStan `bin/stansummary`): bernoulli_fit.summary()
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