Update param_est.ipynb

examples/param_est.ipynb

@@ -203,9 +203,10 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "An additional feature of the `estimate_params()` function is the tolerance feature, or `tol`. The `tol` argument forces our parameter estimation function to continue optimizing until we reach a particular error. This is important for cases where higher model precision is required.\n",
+ "An additional feature of the `estimate_params()` function is the tolerance feature, or `tol`. The exact function that the `tol` argument\n",
+ "uses is specific to the method used. For example, the `tol` argument for the `Nelder-Mead` method is the change in the best error between iterations, while the `tol` argument for the `L-BFGS-B` method is the change in the best parameter between iterations. For more information, see the [scipy.optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html) documentation.\n",
  "\n",
- "In our previous example, note that our total Error was roughly 6e-10 after the `estimate_params()` call. Now, let us see what happens to the parameters when we set a low tolerance and bounds to their respective keys!"
+ "In our previous example, note that our total Error was roughly __6e-10__ after the `estimate_params()` call. Now, let us see what happens to the parameters when we pass a tolerance of __1e-6__."
  ]
  },
  {