How do I specify priors in Stan?
If no prior is defined, Stan uses default priors with the specifications uniform(-infinity, +infinity) . You can restrict priors using upper or lower when declaring the parameters (i.e. lower = 0 > to make sure a parameter is positive).
What is a Stan file?
Stan is an imperative probabilistic programming language. A Stan program defines a probability model. It declares data and (constrained) parameter variables. It defines log posterior (or penalized likelihood).
What is Stan code?
mc-stan.org. Stan is a probabilistic programming language for statistical inference written in C++. The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function. Stan is licensed under the New BSD License.
What are the function of Stan?
Stan allows only numeric data, that is, doubles, integers, and arrays of these. Data of other types (for example, characters and factors) are not passed to Stan. Check whether there are objects in the data list with duplicated names. Duplicated names, if found, will cause the function to stop and report an error.
Which is AutoScale does the prior distribution take?
Each of these functions also takes an argument autoscale. For the prior distribution for the intercept, location, scale, and df should be scalars.
How to calculate the prior scale for a Gaussian model?
First, for Gaussian models only, the prior scales for the intercept, coefficients, and the auxiliary parameter sigma (error standard deviation) are multiplied by sd (y). Additionally — not only for Gaussian models — if the QR argument to the model fitting function (e.g. stan_glm) is FALSE then we also divide the prior scale (s) by sd (x) .
How are the prior scales modified in rstanarm?
If TRUE then the scales of the priors on the intercept and regression coefficients may be additionally modified internally by rstanarm in the following cases. First, for Gaussian models only, the prior scales for the intercept, coefficients, and the auxiliary parameter sigma (error standard deviation) are multiplied by sd (y).
Can a prior distribution be a scalar for the intercept?
For the prior distribution for the intercept, location, scale, and df should be scalars. For the prior for the other coefficients they can either be vectors of length equal to the number of coefficients (not including the intercept), or they can be scalars, in which case they will be recycled to the appropriate length.