minor text improvements (#7361)

Author: alimanfoo

docs/source/learn/core_notebooks/pymc_overview.ipynb

@@ -71,7 +71,7 @@
     "\n",
     "### Generating data\n",
     "\n",
-    "We can simulate some artificial data from this model using only NumPy's {mod}`~numpy.random` module, and then use PyMC to try to recover the corresponding parameters. We are intentionally generating the data to closely correspond the PyMC model structure."
+    "We can simulate some artificial data from this model using only NumPy's {mod}`~numpy.random` module, and then use PyMC to try to recover the corresponding parameters. We are intentionally generating the data to closely correspond to the PyMC model structure."
    ]
   },
   {
@@ -124,7 +124,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here is what the simulated data look like. We use the `pylab` module from the plotting library matplotlib. "
+    "Here is what the simulated data look like. We use the plotting library matplotlib to visualize the data. "
    ]
   },
   {
@@ -2957,7 +2957,7 @@
     "\n",
     "You may have heard of regularization from machine learning or classical statistics applications, where methods like the lasso or ridge regression shrink parameters towards zero by applying a penalty to the size of the regression parameters. In a Bayesian context, we apply an appropriate prior distribution to the regression coefficients. One such prior is the *hierarchical regularized horseshoe*, which uses two regularization strategies, one global and a set of local parameters, one for each coefficient. The key to making this work is by selecting a long-tailed distribution as the shrinkage priors, which allows some to be nonzero, while pushing the rest towards zero.\n",
     "\n",
-    "The horeshoe prior for each regression coefficient $\\beta_i$ looks like this:\n",
+    "The horseshoe prior for each regression coefficient $\\beta_i$ looks like this:\n",
     "\n",
     "$$\\beta_i \\sim N\\left(0, \\tau^2 \\cdot \\tilde{\\lambda}_i^2\\right)$$\n",
     "\n",
@@ -3980,7 +3980,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If your logp can not be expressed in PyTensor, you can decorate the function with `as_op` as follows: `@as_op(itypes=[at.dscalar], otypes=[at.dscalar])`. Note, that this will create a blackbox Python function that will be much slower and  not provide the gradients necessary for e.g. NUTS."
+    "If your logp cannot be expressed in PyTensor, you can decorate the function with `as_op` as follows: `@as_op(itypes=[at.dscalar], otypes=[at.dscalar])`. Note, that this will create a blackbox Python function that will be much slower and  not provide the gradients necessary for e.g. NUTS."
    ]
   },
   {
@@ -4079,7 +4079,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,