Minor formatting in notebooks

2 files changed, 6 insertions, 5 deletions

diff --git a/examples/inference.ipynb b/examples/inference.ipynb
index 129fd86..bfebf25 100644
--- a/examples/inference.ipynb
+++ b/examples/inference.ipynb
@@ -476,7 +476,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One of the great advantages of Bayesian inferences is the access we have to the full posterior distributions. We can visualize the relationships between our model parameters by plotting the joint posterior distributions, as is done here using the [`getdist` package](https://getdist.readthedocs.io/en/latest/)."
+    "One of the great advantages of Bayesian inferences is the access we have to the full posterior distributions. We can visualize the relationships between our model parameters by plotting the joint posterior distributions, as is done here using the [getdist package](https://getdist.readthedocs.io/en/latest/)."
    ]
   },
   {
diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
index 529bb6e..669223e 100644
--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@@ -274,7 +274,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the things we have learned above, we can start to generate some data! Usually, this comes in the form of a likelihood fit comparing IceCube data to our models. GolemFlavor has built in hooks to the [`GolemFit` package](https://github.com/IceCubeOpenSource/GolemFit) for this, however `GolemFit` is only accessible to IceCube collaborators as it contains proprietary code/data. Instead, we can generate some fake data using a multivariate Gaussian likelihood. GolemFlavor has a convenient function to do such a task."
+    "Using the things we have learned above, we can start to generate some data! Usually, this comes in the form of a likelihood fit comparing IceCube data to our models. GolemFlavor has built in hooks to the [GolemFit package](https://github.com/IceCubeOpenSource/GolemFit) for this, however `GolemFit` is only accessible to IceCube collaborators as it contains proprietary code/data. Instead, we can generate some fake data using a multivariate Gaussian likelihood. GolemFlavor has a convenient function to do such a task."
    ]
   },
   {
@@ -422,7 +422,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can define the wrappers to the [`emcee` package](https://emcee.readthedocs.io/en/stable/) which will sample over the flavor angles using an affine invariant MCMC algorithm. To do this, it is convenient to define our parameters using the GolemFlavor `ParamSet` class, as so:"
+    "Now we can define the wrappers to the [emcee package](https://emcee.readthedocs.io/en/stable/) which will sample over the flavor angles using an affine invariant MCMC algorithm. To do this, it is convenient to define our parameters using the GolemFlavor `ParamSet` class, as so:"
    ]
   },
   {
@@ -463,8 +463,9 @@
    "metadata": {},
    "source": [
     "Here we have 2 `ParamSet` objects:\n",
-    "* `asimov_paramset` contains the measured parameters\n",
-    "* `llh_paramset` contains the model parameter values\n",
+    "\n",
+    "- `asimov_paramset` contains the measured parameters\n",
+    "- `llh_paramset` contains the model parameter values\n",
     "\n",
     "In this example, they contain the same parameters since we are doing a simple scan over the measured flavor angles to generate some fake data."
    ]