Update docstring in ChiSquared, HalfNormal, Wald, Pareto, ExGaussion and VonMiese distribution (#5595)

* Fixed ChiSquared, HalfNormal, Wald, Pareto, ExGaussion and VonMiese docstring

* fixed optional parameter

* fixed TensorVariable

Author: purna135

pymc/distributions/continuous.py

@@ -818,9 +818,10 @@ class HalfNormal(PositiveContinuous):
        the standard deviation/precision of the unfolded normal distribution, for
        the standard deviation of the half-normal distribution, see below. For
        the half-normal, they are just two parameterisation :math:`\sigma^2
-       \equiv \frac{1}{\tau}` of a scale parameter
+       \equiv \frac{1}{\tau}` of a scale parameter.
 
     .. plot::
+        :context: close-figs
 
         import matplotlib.pyplot as plt
         import numpy as np
@@ -844,10 +845,12 @@ class HalfNormal(PositiveContinuous):
 
     Parameters
     ----------
-    sigma: float
-        Scale parameter :math:`sigma` (``sigma`` > 0) (only required if ``tau`` is not specified).
-    tau: float
-        Precision :math:`tau` (tau > 0) (only required if sigma is not specified).
+    sigma : tensor_like of float, optional
+        Scale parameter :math:`\sigma` (``sigma`` > 0) (only required if ``tau`` is not specified).
+        Defaults to 1.
+    tau : tensor_like of float, optional
+        Precision :math:`\tau` (tau > 0) (only required if sigma is not specified).
+        Defaults to 1.
 
     Examples
     --------
@@ -883,7 +886,7 @@ def logcdf(value, loc, sigma):
 
         Parameters
         ----------
-        value: numeric or np.ndarray or aesara.tensor
+        value : tensor_like of float
             Value(s) for which log CDF is calculated. If the log CDF for multiple
             values are desired the values must be provided in a numpy array or Aesara tensor.
 
@@ -935,6 +938,7 @@ class Wald(PositiveContinuous):
            \right\}
 
     .. plot::
+        :context: close-figs
 
         import matplotlib.pyplot as plt
         import numpy as np
@@ -967,13 +971,13 @@ class Wald(PositiveContinuous):
 
     Parameters
     ----------
-    mu: float, optional
+    mu : tensor_like of float, optional
         Mean of the distribution (mu > 0).
-    lam: float, optional
+    lam : tensor_like of float, optional
         Relative precision (lam > 0).
-    phi: float, optional
+    phi : tensor_like of float, optional
         Alternative shape parameter (phi > 0).
-    alpha: float, optional
+    alpha : tensor_like of float, default 0
         Shift/location parameter (alpha >= 0).
 
     Notes
@@ -1060,14 +1064,14 @@ def logp(
 
         Parameters
         ----------
-        value: numeric
+        value : tensor_like of float
             Value(s) for which log-probability is calculated. If the log probabilities for multiple
-            values are desired the values must be provided in a numpy array or Aesara tensor
-        mu: float or TensorVariable
+            values are desired the values must be provided in a numpy array or Aesara tensor.
+        mu : tensor_like of float
             Mean of the distribution (mu > 0).
-        lam: float or TensorVariable
+        lam : tensor_like of float
             Relative precision (lam > 0).
-        alpha: float or TensorVariable
+        alpha : tensor_like of float
             Shift/location parameter (alpha >= 0).
 
         Returns
@@ -1105,14 +1109,14 @@ def logcdf(
 
         Parameters
         ----------
-        value: numeric or np.ndarray or aesara.tensor
+        value : tensor_like of float
             Value(s) for which log CDF is calculated. If the log CDF for multiple
             values are desired the values must be provided in a numpy array or Aesara tensor.
-        mu: float or TensorVariable
+        mu : tensor_like of float
             Mean of the distribution (mu > 0).
-        lam: float or TensorVariable
+        lam : tensor_like of float
             Relative precision (lam > 0).
-        alpha: float or TensorVariable
+        alpha : tensor_like of float
             Shift/location parameter (alpha >= 0).
 
         Returns
@@ -1982,6 +1986,7 @@ class Pareto(BoundedContinuous):
        f(x \mid \alpha, m) = \frac{\alpha m^{\alpha}}{x^{\alpha+1}}
 
     .. plot::
+        :context: close-figs
 
         import matplotlib.pyplot as plt
         import numpy as np
@@ -2008,9 +2013,9 @@ class Pareto(BoundedContinuous):
 
     Parameters
     ----------
-    alpha: float
+    alpha : tensor_like of float
         Shape parameter (alpha > 0).
-    m: float
+    m : tensor_like of float
         Scale parameter (m > 0).
     """
     rv_op = pareto
@@ -2045,7 +2050,7 @@ def logcdf(
 
         Parameters
         ----------
-        value: numeric or np.ndarray or aesara.tensor
+        value : tensor_like of float
             Value(s) for which log CDF is calculated. If the log CDF for multiple
             values are desired the values must be provided in a numpy array or Aesara tensor.
 
@@ -2493,6 +2498,7 @@ class ChiSquared(PositiveContinuous):
        f(x \mid \nu) = \frac{x^{(\nu-2)/2}e^{-x/2}}{2^{\nu/2}\Gamma(\nu/2)}
 
     .. plot::
+        :context: close-figs
 
         import matplotlib.pyplot as plt
         import numpy as np
@@ -2517,7 +2523,7 @@ class ChiSquared(PositiveContinuous):
 
     Parameters
     ----------
-    nu: float
+    nu : tensor_like of float
         Degrees of freedom (nu > 0).
     """
     rv_op = chisquare
@@ -2540,10 +2546,11 @@ def logcdf(value, nu):
 
         Parameters
         ----------
-        value: numeric or np.ndarray or `TensorVariable`
+        value : tensor_like of float
             Value(s) for which log CDF is calculated. If the log CDF for
             multiple values are desired the values must be provided in a numpy
-            array or `TensorVariable`.
+            array or Aesara tensor.
+
         Returns
         -------
         TensorVariable
@@ -2813,6 +2820,7 @@ class ExGaussian(Continuous):
     standard normal distribution.
 
     .. plot::
+        :context: close-figs
 
         import matplotlib.pyplot as plt
         import numpy as np
@@ -2839,11 +2847,11 @@ class ExGaussian(Continuous):
 
     Parameters
     ----------
-    mu: float
+    mu : tensor_like of float, default 0
         Mean of the normal distribution.
-    sigma: float
+    sigma : tensor_like of float
         Standard deviation of the normal distribution (sigma > 0).
-    nu: float
+    nu : tensor_like of float
         Mean of the exponential distribution (nu > 0).
 
     References
@@ -2884,9 +2892,9 @@ def logp(value, mu, sigma, nu):
 
         Parameters
         ----------
-        value: numeric
+        value : tensor_like of float
             Value(s) for which log-probability is calculated. If the log probabilities for multiple
-            values are desired the values must be provided in a numpy array or Aesara tensor
+            values are desired the values must be provided in a numpy array or Aesara tensor.
 
         Returns
         -------
@@ -2924,7 +2932,7 @@ def logcdf(value, mu, sigma, nu):
 
         Parameters
         ----------
-        value: numeric or np.ndarray or aesara.tensor
+        value : tensor_like of float
             Value(s) for which log CDF is calculated. If the log CDF for multiple
             values are desired the values must be provided in a numpy array or Aesara tensor.
 
@@ -2964,6 +2972,7 @@ class VonMises(CircularContinuous):
     where :math:`I_0` is the modified Bessel function of order 0.
 
     .. plot::
+        :context: close-figs
 
         import matplotlib.pyplot as plt
         import numpy as np
@@ -2989,9 +2998,9 @@ class VonMises(CircularContinuous):
 
     Parameters
     ----------
-    mu: float
+    mu : tensor_like of float, default 0
         Mean.
-    kappa: float
+    kappa : tensor_like of float
         Concentration (\frac{1}{kappa} is analogous to \sigma^2).
     """