Minor formatting in notebooks

1 files changed, 5 insertions, 4 deletions

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."
    ]