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VOOZH | about |
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VOOZH | about |
Stan enables sophisticated statistical modeling using Bayesian inference, allowing for more accurate and interpretable results in complex data scenarios.
Stan’s probabilistic programming language is suitable for a wide range of applications, from simple linear regression to multi-level models and time-series analysis.
Interfaces for Python, Julia, R, and the Unix shell make it easy to use Stan in any programming environment, on laptops, clusters, or in the cloud. A rich ecosystem of tools for validation and visualization support decision-making and communication.
bernoulli.stan
data {
int<lower=0> N;
array[N] int<lower=0, upper=1> y;
}
parameters {
real<lower=0, upper=1> theta;
}
model {
// uniform prior on interval 0,1
theta ~ beta(1, 1);
y ~ bernoulli(theta);
}The Stan forums provide support for all user levels and topics, from installing software, to writing Stan programs, to advanced Bayesian modeling techniques and methodology.
Stan’s documentation, tutorials, and case studies help users learn and use Stan effectively in their own projects. The Prior Choice Recommendations wiki page provides guidance on appropriate priors for use with Stan.
The Stan slack channel mc-stan.slack.com is for informal developer discussions.