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## 18.5 User-Defined Probability Functions

Probability functions are distinguished in Stan by names ending in _lpdf for density functions and _lpmf for mass functions; in both cases, they must have real return types.

Suppose a model uses several standard normal distributions, for which there is not a specific overloaded density nor defaults in Stan. So rather than writing out the location of 0 and scale of 1 for all of them, a new density function may be defined and reused.

functions {
real unit_normal_lpdf(real y) {
return normal_lpdf(y | 0, 1);
}
}
...
model {
alpha ~ unit_normal();
beta ~ unit_normal();
...
}

The ability to use the unit_normal function as a density is keyed off its name ending in _lpdf (names ending in _lpmf for probability mass functions work the same way).

In general, if foo_lpdf is defined to consume $$N + 1$$ arguments, then

y ~ foo(theta1, ..., thetaN);

can be used as shorthand for

target += foo_lpdf(y | theta1, ..., thetaN);

As with the built-in functions, the suffix _lpdf is dropped and the first argument moves to the left of the sampling symbol (~) in the sampling statement.

Functions ending in _lpmf (for probability mass functions), behave exactly the same way. The difference is that the first argument of a density function (_lpdf) must be continuous (not an integer or integer array), whereas the first argument of a mass function (_lpmf) must be discrete (integer or integer array).