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20.2 Special-case distribution issues

Pedantic mode checks for some specific uses of distributions that may indicate a statistical mistake:

20.2.1 Uniform distributions

Any use of uniform distribution generates a warning, except when the variate parameter’s declared upper and lower bounds exactly match the uniform distribution bounds. In general, assigning a parameter a uniform distribution can create non-differentiable boundary conditions and is not recommended.

For example, consider the following program.

parameters {
  real a;
  real<lower=0, upper=1> b;
model {
  a ~ uniform(0, 1);
  b ~ uniform(0, 1);

a is assigned a uniform distribution that doesn’t match its constraints.

Pedantic mode produces the following warning.

Warning at 'uniform-warn.stan', line 6, column 2 to column 20:
  Parameter a is given a uniform distribution. The uniform distribution is
  not recommended, for two reasons: (a) Except when there are logical or
  physical constraints, it is very unusual for you to be sure that a
  parameter will fall inside a specified range, and (b) The infinite gradient
  induced by a uniform density can cause difficulties for Stan's sampling
  algorithm. As a consequence, we recommend soft constraints rather than hard
  constraints; for example, instead of giving an elasticity parameter a
  uniform(0,1) distribution, try normal(0.5,0.5).

20.2.2 (Inverse-) Gamma distributions

Gamma distributions are sometimes used as an attempt to assign an improper prior to a parameter. Pedantic mode gives a warning when the Gamma arguments indicate that this may be the case.

20.2.3 lkj_corr distribution

Any use of the lkj_corr distribution generates a warning that suggests using the Cholesky variant instead. See for details.