Make StudentT/AsymmetricLaplace .dist() signatures consistent

RELEASE-NOTES.md

@@ -26,6 +26,10 @@ Also check out the [milestones](https://github.com/pymc-devs/pymc/milestones) fo
 
 All of the above apply to:
 
+- `pm.StudentT` now now requires either `sigma` or `lam` as kwarg [#5628](https://github.com/pymc-devs/pymc/pull/5628)
+- `pm.StudentT` now requires `nu` to be specified (no longer defaults to 1)
+- `pm.AsymmetricLaplace` positional arguments re-ordered
+- `pm.AsymmetricLaplace` now requires `mu` to be specified (no longer defaults to 0)
 - BART was removed [#5566](https://github.com/pymc-devs/pymc/pull/5566). It is now available from [pymc-experimental](https://github.com/pymc-devs/pymc-experimental)
 - `BaseStochasticGradient` was removed (see [#5630](https://github.com/pymc-devs/pymc/pull/5630))
 - ⚠ The library is now named, installed and imported as "pymc". For example: `pip install pymc`.

pymc/distributions/continuous.py

@@ -1644,12 +1644,12 @@ class AsymmetricLaplace(Continuous):
 
     Parameters
     ----------
-    b : tensor_like of float
-        Scale parameter (b > 0).
     kappa : tensor_like of float
         Symmetry parameter (kappa > 0).
     mu : tensor_like of float, default 0
         Location parameter.
+    b : tensor_like of float
+        Scale parameter (b > 0).
 
     See Also:
     --------
@@ -1658,7 +1658,7 @@ class AsymmetricLaplace(Continuous):
     rv_op = asymmetriclaplace
 
     @classmethod
-    def dist(cls, b, kappa, mu=0, *args, **kwargs):
+    def dist(cls, kappa, mu, b, *args, **kwargs):
         b = at.as_tensor_variable(floatX(b))
         kappa = at.as_tensor_variable(floatX(kappa))
         mu = mu = at.as_tensor_variable(floatX(mu))
@@ -1897,7 +1897,7 @@ class StudentT(Continuous):
     rv_op = studentt
 
     @classmethod
-    def dist(cls, nu, mu=0, lam=None, sigma=None, *args, **kwargs):
+    def dist(cls, nu, mu=0, *, sigma=None, lam=None, **kwargs):
         nu = at.as_tensor_variable(floatX(nu))
         lam, sigma = get_tau_sigma(tau=lam, sigma=sigma)
         sigma = at.as_tensor_variable(sigma)
@@ -2705,7 +2705,7 @@ class HalfStudentT(PositiveContinuous):
 
     Parameters
     ----------
-    nu : tensor_like of float, default 1
+    nu : tensor_like of float
         Degrees of freedom, also known as normality parameter (nu > 0).
     sigma : tensor_like of float, optional
         Scale parameter (sigma > 0). Converges to the standard deviation as nu
@@ -2728,7 +2728,7 @@ class HalfStudentT(PositiveContinuous):
     rv_op = halfstudentt
 
     @classmethod
-    def dist(cls, nu=1, sigma=None, lam=None, *args, **kwargs):
+    def dist(cls, nu, sigma=None, lam=None, *args, **kwargs):
         nu = at.as_tensor_variable(floatX(nu))
         lam, sigma = get_tau_sigma(lam, sigma)
         sigma = at.as_tensor_variable(sigma)

pymc/tests/test_distributions.py

@@ -1617,6 +1617,7 @@ def test_half_studentt(self):
             Rplus,
             {"sigma": Rplus},
             lambda value, sigma: sp.halfcauchy.logpdf(value, 0, sigma),
+            extra_args={"nu": 1},
         )
 
     def test_skew_normal(self):