Merge branch 'pymc-devs:main' into debug_docs_and_function_helpers

Author: itsdivya1309

README.rst

@@ -46,11 +46,11 @@ If you already know about Bayesian statistics:
 Learn Bayesian statistics with a book together with PyMC
 --------------------------------------------------------
 
+-  `Bayesian Analysis with Python  <http://bap.com.ar/>`__ (third edition) by Osvaldo Martin: Great introductory book.
 -  `Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>`__: Fantastic book with many applied code examples.
 -  `PyMC port of the book "Doing Bayesian Data Analysis" by John Kruschke <https://github.com/cluhmann/DBDA-python>`__ as well as the `first edition <https://github.com/aloctavodia/Doing_bayesian_data_analysis>`__.
 -  `PyMC port of the book "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath <https://github.com/pymc-devs/resources/tree/master/Rethinking>`__
 -  `PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers <https://github.com/pymc-devs/resources/tree/master/BCM>`__: Focused on using Bayesian statistics in cognitive modeling.
--  `Bayesian Analysis with Python  <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition>`__ (second edition) by Osvaldo Martin: Great introductory book. (`code <https://github.com/aloctavodia/BAP>`__ and errata).
 
 Audio & Video
 -------------

docs/source/learn.md

@@ -13,8 +13,9 @@ glossary
 
 ## At a glance
 ### Beginner
+  - Book: [Bayesian Analysis with Python](http://bap.com.ar/)
   - Book: [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)
-  - Book: [Bayesian Analysis with Python](https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition)
+
 
 ### Intermediate
   - {ref}`pymc_overview` shows PyMC 4.0 code in action

pymc/distributions/bound.py

@@ -27,7 +27,6 @@
 from pymc.distributions.transforms import _default_transform
 from pymc.logprob.basic import logp
 from pymc.model import modelcontext
-from pymc.pytensorf import floatX, intX
 from pymc.util import check_dist_not_registered
 
 __all__ = ["Bound"]
@@ -206,7 +205,7 @@ def __new__(
             res = _ContinuousBounded(
                 name,
                 [dist, lower, upper],
-                initval=floatX(initval),
+                initval=initval.astype("float"),
                 size=size,
                 shape=shape,
                 **kwargs,
@@ -215,7 +214,7 @@ def __new__(
             res = _DiscreteBounded(
                 name,
                 [dist, lower, upper],
-                initval=intX(initval),
+                initval=initval.astype("int"),
                 size=size,
                 shape=shape,
                 **kwargs,
@@ -241,15 +240,15 @@ def dist(
                 shape=shape,
                 **kwargs,
             )
-            res.tag.test_value = floatX(initval)
+            res.tag.test_value = initval
         else:
             res = _DiscreteBounded.dist(
                 [dist, lower, upper],
                 size=size,
                 shape=shape,
                 **kwargs,
             )
-            res.tag.test_value = intX(initval)
+            res.tag.test_value = initval.astype("int")
         return res
 
     @classmethod
@@ -286,9 +285,9 @@ def _set_values(cls, lower, upper, size, shape, initval):
             size = shape
 
         lower = np.asarray(lower)
-        lower = floatX(np.where(lower == None, -np.inf, lower))  # noqa E711
+        lower = np.where(lower == None, -np.inf, lower)  # noqa E711
         upper = np.asarray(upper)
-        upper = floatX(np.where(upper == None, np.inf, upper))  # noqa E711
+        upper = np.where(upper == None, np.inf, upper)  # noqa E711
 
         if initval is None:
             _size = np.broadcast_shapes(to_tuple(size), np.shape(lower), np.shape(upper))
@@ -303,7 +302,6 @@ def _set_values(cls, lower, upper, size, shape, initval):
                     np.where(_upper == np.inf, _lower + 1, (_lower + _upper) / 2),
                 ),
             )
-
-        lower = as_tensor_variable(floatX(lower))
-        upper = as_tensor_variable(floatX(upper))
+        lower = as_tensor_variable(lower, dtype="floatX")
+        upper = as_tensor_variable(upper, dtype="floatX")
         return lower, upper, initval