diff --git a/docs/notebooks/linear_gaussian_ssm/lgssm_learning.ipynb b/docs/notebooks/linear_gaussian_ssm/lgssm_learning.ipynb
index d0d0b103..b7ba8794 100644
--- a/docs/notebooks/linear_gaussian_ssm/lgssm_learning.ipynb
+++ b/docs/notebooks/linear_gaussian_ssm/lgssm_learning.ipynb
@@ -10,12 +10,12 @@
     "\n",
     "This notebook shows how to \"fit\" a linear Gaussian SSM — i.e., estimate the parameters and infer the latent states — using either expectation-maximization (EM) or stochastic gradient descent (SGD) on the negative log marginal likelihood of the data. \n",
     "\n",
-    "Here, we work with simulate noisy data from an LG-SSM with known parameters, and then we see how well we can recover the true parameters and states given the observations. The model is,\n",
+    "Here, we work with simulated noisy data from an LG-SSM with known parameters, and then we see how well we can recover the true parameters and states given the observations. The model is,\n",
     "\\begin{align*}\n",
     "z_{t+1} \\mid z_t, \\theta &\\sim \\mathrm{N}(F z_t, Q) \\\\\n",
     "y_t \\mid z_t, \\theta &\\sim \\mathrm{N}(H z_t, R)\n",
     "\\end{align*}\n",
-    "where $z_{1:T}$ are the latent states, $y_{1:T}$ are the emissions, and $\\theta = (F, Q, H, R)$ are the model parameters. In particular, $F$ is the dynamics matrix and $H$ is the emission matrix. For our simulation, we use 2-dimensional latent states, $z_t \\in \\mathbb{R}^2$, and 10-dimensional emissions, $y_t \\in \\mathbb{R}^10$. \n",
+    "where $z_{1:T}$ are the latent states, $y_{1:T}$ are the emissions, and $\\theta = (F, Q, H, R)$ are the model parameters. In particular, $F$ is the dynamics matrix and $H$ is the emission matrix. For our simulation, we use 2-dimensional latent states, $z_t \\in \\mathbb{R}^2$, and 10-dimensional emissions, $y_t \\in \\mathbb{R}^{10}$. \n",
     "\n",
     "We fit the model to estimate parameters, $\\hat{\\theta}$, using either EM or SGD, as shown below. Once we have estimated the paraemeters, we can also infer the latent states given those parameters.\n"
    ]