diff --git a/demo_radio.ipynb b/demo_radio.ipynb
index 55930a0c2814672a481b74f5d94d096953f9a617..ef40ca17af3b0638dd93a7beab54dc68d12ae891 100644
--- a/demo_radio.ipynb
+++ b/demo_radio.ipynb
@@ -78,7 +78,7 @@
     "Model of the sky brightness distribution\n",
     "----------------------------------------\n",
     "\n",
-    "The sky model is going to be a log-normal random process (the log-brightness is Gaussian distributed). As the prior correlation structure of the log-brightness is unkown, it will be generated using a `CorrelatedField` model and the power-spectrum is inferred along with the realization."
+    "The sky model is going to be a log-normal random process (the log-brightness is Gaussian distributed). As the prior correlation structure of the log-brightness is unknown, it will be generated using a `CorrelatedField` model and the power-spectrum is inferred along with the realization."
    ]
   },
   {
@@ -208,14 +208,14 @@
     "Solve the inference problem\n",
     "---------------------------\n",
     "\n",
-    "The `likelihood` together with the `sky_model` fully specify a Bayesian inverse problem and imply a posterior probabiltiy distribution over the degrees of freedom (DOF) of the model. This distribution is in general a high-dimensional (number of pixels + DOF of power spectrum) and non-Gaussian distribution which prohibits analytical integration. To access its information and compute posterior expectation values numerical approximations have to be made.\n",
+    "The `likelihood` together with the `sky_model` fully specify a Bayesian inverse problem and imply a posterior probability distribution over the degrees of freedom (DOF) of the model. This distribution is, in general, a high-dimensional (number of pixels + DOF of power spectrum) and non-Gaussian distribution, which prohibits analytical integration. To access its information and compute posterior expectation values, numerical approximations have to be made.\n",
     "\n",
-    "`nifty` provides multiple ways of psterior approximation, with Variational Inference (VI) being by far the most frequently used method. In VI the posterior distribution is approximated with another distribution by minimizing their respective forward Kullbach-Leibler divergence (KL). In the following, the Geometric VI method is employed which utilizes conceps of differential geometry to provide a local estimate of the distribution function.\n",
+    "`nifty` provides multiple ways of posterior approximation, with Variational Inference (VI) being by far the most frequently used method. In VI the posterior distribution is approximated with another distribution by minimizing their respective forward Kullbach-Leibler divergence (KL). In the following, the Geometric VI method is employed which utilizes concepts of differential geometry to provide a local estimate of the distribution function.\n",
     "\n",
-    "Its numerical implementation (`ift.optimize_kl`) consists of a repeated and successive re-approximation of the VI objective function (the KL) via a stochastic estimate. This estimate is generated using the at the time best available approximation of the posterior, and then the KL gets minimized to further improve it. The resulting algorithm cosists of a repeated re-generation of novel samples for the estimate and a successing optimization thereof until convergence is reached.\n",
+    "Its numerical implementation (`ift.optimize_kl`) consists of a repeated and successive re-approximation of the VI objective function (the KL) via a stochastic estimate. This estimate is generated using the at the time best available approximation of the posterior, and then the KL gets minimized to further improve it. The resulting algorithm consists of a repeated re-generation of novel samples for the estimate and a successing optimization thereof until convergence is reached.\n",
     "\n",
     "The internal steps of `ift.optimize_kl` invoke the approximate solution of multiple interdependent optimization problems:\n",
-    "- For sample generation, a linear system of eqations is approximated using the `ConjugateGradient` (CG) method\n",
+    "- For sample generation, a linear system of equations is approximated using the `ConjugateGradient` (CG) method\n",
     "- Furthermore, the sample generation invokes a non-linear optimization problem approximated using the `NewtonCG` method\n",
     "- Finally, the approximative distribution is optimized by minimizing the KL between the true posterior and the approximation. This again invokes a non-linear optimization problem approximated with `NewtonCG`."
    ]
@@ -227,9 +227,9 @@
     "Posterior visualization\n",
     "-----------------------\n",
     "\n",
-    "Before we set run the minimization routine, we set up a `plotting_callback` function for visualization. Note that additional information and plots regarding the reconstruction are generated during an `ift.optimize_kl` run and stored in the folder passed to the `output_directory` argument of `ift.optimize_kl`\n",
+    "Before we run the minimization routine, we set up a `plotting_callback` function for visualization. Note that additional information and plots regarding the reconstruction are generated during an `ift.optimize_kl` run and stored in the folder passed to the `output_directory` argument of `ift.optimize_kl`\n",
     "The final output of `ift.optimize_kl` is a collection of approximate posterior samples and is provided via an instance of `ift.ResidualSampleList`. A `SampleList` provides a variety of convenience functions such as: \n",
-    "- `average`: to compute samples averages\n",
+    "- `average`: to compute sample averages\n",
     "- `sample_stat`: to get the approximate mean and variance of a model\n",
     "- `iterator`: a python iterator over all samples\n",
     "- ..."
@@ -399,7 +399,7 @@
     "UV - Uncertainty map\n",
     "--------------------\n",
     "\n",
-    "We can generate and study the posterior uncertainty maps for any kind on quantity we are interested in (i.E. not only for the sky brightness). In particular, we can also take a look at the uncertainty of the sky brightness in the UV plane. Comparing this to the measured UV-tracks yields information "
+    "We can generate and study the posterior uncertainty maps for any kind of quantity we are interested in (i.e., not only for the sky brightness). In particular, we can also take a look at the uncertainty of the sky brightness in the UV plane. Comparing this to the measured UV-tracks is insghtfull. "
    ]
   },
   {
@@ -464,6 +464,20 @@
     "uv_plot(np.log(uv_var / uv_gt), pre = 'relative ')\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -488,7 +502,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.6"
   },
   "vscode": {
    "interpreter": {
@@ -497,5 +511,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }