replace optimisation function argument of find_MAP (#396)

* update function argument

Co-authored-by: Oriol (ZBook) <oriol.abril.pla@gmail.com>

Author: keesterbrugge

examples/gaussian_processes/GP-smoothing.ipynb

@@ -207,7 +207,7 @@
     "def infer_z(smoothing):\n",
     "    with model:\n",
     "        smoothing_param.set_value(smoothing)\n",
-    "        res = pm.find_MAP(vars=[z], fmin=optimize.fmin_l_bfgs_b)\n",
+    "        res = pm.find_MAP(vars=[z], method=\"L-BFGS-B\")\n",
     "        return res[\"z\"]"
    ]
   },

myst_nbs/gaussian_processes/GP-smoothing.myst.md

@@ -121,7 +121,7 @@ Let's also make a helper function for inferring the most likely values of $z$:
 def infer_z(smoothing):
     with model:
         smoothing_param.set_value(smoothing)
-        res = pm.find_MAP(vars=[z], fmin=optimize.fmin_l_bfgs_b)
+        res = pm.find_MAP(vars=[z], method="L-BFGS-B")
         return res["z"]
 ```
 
@@ -155,7 +155,7 @@ title(f"Smoothing={smoothing}");
 
 ### Smoothing "to the limits"
 
-By increasing the smoothing parameter, we can gradually make the inferred values of the hidden Brownian motion approach the average value of the data. This is because as we increase the smoothing parameter, we allow less and less of the variance to be allocated to the Brownian motion, so eventually it approaches the process which almost doesn't change over the domain of $x$:
+By increasing the smoothing parameter, we can gradually make the inferred values of the hidden Brownian motion approach the average value of the data. This is because as we increase the smoothing parameter, we allow less and less of the variance to be allocated to the Brownian motion, so eventually it approaches the process which almost doesn't change over the domain of $x$:
 
 ```{code-cell} ipython3
 fig, axes = subplots(2, 2)