Get L(Prior R

Priors can be defined by set_prior("<prior>", class = "ar") for ar and similar for other autocorrelation parameters.

The default prior is the same as for standard deviations of group-level effects. Gaussian processes as currently implemented in brms have two parameters, the standard deviation parameter sdgp , and characteristic length-scale parameter lscale (see gp for more details).

There are two types of priors: informative and noninformative (or "reference"). Box and Tiao (1973) define a noninformative prior as one that provides little information relative to the experiment - in this case the stock assessment data.

A default prior is a density or relative density that is used as a weight function applied to an observed likelihood function. The choice of prior is based directly on assumed smoothness in the model and an absence of information about how the parameter value was generated.

Prior probability, in Bayesian statistics, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed. Prior probability can be compared with posterior probability.

An uninformative, flat, or diffuse prior expresses vague or general information about a variable. The term "uninformative prior" is somewhat of a misnomer. Such a prior might also be called a not very informative prior, or an objective prior, i.e. one that's not subjectively elicited.

An informative prior expresses specific, definite information about a variable. An example is a prior distribution for the temperature at noon tomorrow.