diff --git a/lectures/ar1_bayes.md b/lectures/ar1_bayes.md
index e553b38c8..8f0146e0c 100644
--- a/lectures/ar1_bayes.md
+++ b/lectures/ar1_bayes.md
@@ -11,7 +11,7 @@ kernelspec:
   name: python3
 ---
 
-# Posterior Distributions for  AR(1) Parameters
+# Posterior Distributions for AR(1) Parameters
 
 ```{include} _admonition/gpu.md
 ```
@@ -19,7 +19,7 @@ kernelspec:
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install numpyro jax
+!pip install numpyro
 ```
 
 In addition to what's included in base Anaconda, we need to install the following packages
@@ -68,7 +68,8 @@ $$
 y_{t+1} = \rho y_t + \sigma_x \epsilon_{t+1}, \quad t \geq 0
 $$ (eq:themodel)
 
-where the scalars $\rho$ and $\sigma_x$ satisfy $|\rho| < 1$ and $\sigma_x > 0$;
+where the scalars $\rho$ and $\sigma_x$ satisfy $|\rho| < 1$ and $\sigma_x > 0$.
+
 $\{\epsilon_{t+1}\}$ is a sequence of i.i.d. normal random variables with mean $0$ and variance $1$.
 
 The second component of the statistical model is
@@ -115,16 +116,23 @@ Unknown parameters are $\rho, \sigma_x$.
 
 We have  independent **prior probability distributions** for $\rho, \sigma_x$ and want to compute a posterior probability distribution after observing a sample $\{y_{t}\}_{t=0}^T$.
 
-The notebook uses `pymc4` and `numpyro` to compute a posterior distribution of $\rho, \sigma_x$. We will use NUTS samplers to generate samples from the posterior in a chain. Both of these libraries support NUTS samplers.
+The notebook uses `pymc4` and `numpyro` to compute a posterior distribution of $\rho, \sigma_x$.
+
+We will use NUTS samplers to generate samples from the posterior in a chain.
+
+Both of these libraries support NUTS samplers.
+
+NUTS is a form of Monte Carlo Markov Chain (MCMC) algorithm that bypasses random walk behaviour and allows for convergence to a target distribution more quickly.
 
-NUTS is a form of Monte Carlo Markov Chain (MCMC) algorithm that bypasses random walk behaviour and allows for convergence to a target distribution more quickly. This not only has the advantage of speed, but allows for complex models to be fitted without having to employ specialised knowledge regarding the theory underlying those fitting methods.
+This not only has the advantage of speed, but allows for complex models to be fitted without having to employ specialised knowledge regarding the theory underlying those fitting methods.
 
 Thus, we explore consequences of making these alternative assumptions about the distribution of $y_0$:
 
-- A first procedure is to condition on whatever value of $y_0$ is observed. This amounts to assuming that the probability distribution of the random variable  $y_0$ is a Dirac delta function that puts probability one on the observed value of $y_0$.
+- A first procedure is to condition on whatever value of $y_0$ is observed.
 
-- A second procedure  assumes that $y_0$ is drawn from the stationary distribution of a process described by {eq}`eq:themodel`
-so that  $y_0 \sim {\cal N} \left(0, {\sigma_x^2\over (1-\rho)^2} \right) $
+This amounts to assuming that the probability distribution of the random variable  $y_0$ is a Dirac delta function that puts probability one on the observed value of $y_0$.
+
+- A second procedure  assumes that $y_0$ is drawn from the stationary distribution of a process described by {eq}`eq:themodel` so that  $y_0 \sim {\cal N} \left(0, {\sigma_x^2\over (1-\rho)^2} \right) $
 
 When the initial value $y_0$ is far out in a tail of the stationary distribution, conditioning on an initial value gives a posterior that is **more accurate** in a sense that we'll explain.
 
@@ -137,7 +145,11 @@ We begin by solving a **direct problem** that simulates an AR(1) process.
 
 How we select the initial value $y_0$ matters.
 
-   * If we think $y_0$ is drawn from the stationary distribution ${\mathcal N}(0, \frac{\sigma_x^{2}}{1-\rho^2})$, then it is a good idea to use this distribution as $f(y_0)$.  Why? Because $y_0$ contains information about $\rho, \sigma_x$.
+   * If we think $y_0$ is drawn from the stationary distribution ${\mathcal N}(0, \frac{\sigma_x^{2}}{1-\rho^2})$, then it is a good idea to use this distribution as $f(y_0)$.
+
+Why?
+
+Because $y_0$ contains information about $\rho, \sigma_x$.
 
    * If we suspect that $y_0$ is far in the tails of the stationary distribution -- so that variation in early observations in the sample have a significant **transient component** -- it is better to condition on $y_0$ by setting $f(y_0) = 1$.
 
@@ -146,25 +158,25 @@ To illustrate the issue, we'll begin by choosing an initial $y_0$ that is far ou
 
 ```{code-cell} ipython3
 
-def ar1_simulate(rho, sigma, y0, T):
+def ar1_simulate(ρ, σ, y0, T):
 
     # Allocate space and draw epsilons
     y = np.empty(T)
-    eps = np.random.normal(0.,sigma,T)
+    eps = np.random.normal(0., σ, T)
 
     # Initial condition and step forward
     y[0] = y0
     for t in range(1, T):
-        y[t] = rho*y[t-1] + eps[t]
+        y[t] = ρ * y[t-1] + eps[t]
 
     return y
 
-sigma =  1.
-rho = 0.5
+σ = 1.
+ρ = 0.5
 T = 50
 
 np.random.seed(145353452)
-y = ar1_simulate(rho, sigma, 10, T)
+y = ar1_simulate(ρ, σ, 10, T)
 ```
 
 ```{code-cell} ipython3
@@ -178,7 +190,7 @@ Now we shall use Bayes' law to construct a posterior distribution, conditioning
 
 First we'll use **pymc4**.
 
-## PyMC Implementation
+## PyMC implementation
 
 For a normal distribution in `pymc`,
 $var = 1/\tau = \sigma^{2}$.
@@ -190,14 +202,14 @@ AR1_model = pmc.Model()
 with AR1_model:
 
     # Start with priors
-    rho = pmc.Uniform('rho', lower=-1., upper=1.) # Assume stable rho
-    sigma = pmc.HalfNormal('sigma', sigma = np.sqrt(10))
+    ρ = pmc.Uniform('rho', lower=-1., upper=1.) # Assume stable rho
+    σ = pmc.HalfNormal('sigma', sigma=np.sqrt(10))
 
     # Expected value of y at the next period (rho * y)
-    yhat = rho * y[:-1]
+    yhat = ρ * y[:-1]
 
     # Likelihood of the actual realization
-    y_like = pmc.Normal('y_obs', mu=yhat, sigma=sigma, observed=y[1:])
+    y_like = pmc.Normal('y_obs', mu=yhat, sigma=σ, observed=y[1:])
 ```
 
 [pmc.sample](https://www.pymc.io/projects/docs/en/v5.10.0/api/generated/pymc.sample.html#pymc-sample) by default uses the NUTS samplers to generate samples as shown in the below cell:
@@ -246,15 +258,15 @@ AR1_model_y0 = pmc.Model()
 with AR1_model_y0:
 
     # Start with priors
-    rho = pmc.Uniform('rho', lower=-1., upper=1.) # Assume stable rho
-    sigma = pmc.HalfNormal('sigma', sigma=np.sqrt(10))
+    ρ = pmc.Uniform('rho', lower=-1., upper=1.) # Assume stable rho
+    σ = pmc.HalfNormal('sigma', sigma=np.sqrt(10))
 
     # Standard deviation of ergodic y
-    y_sd = sigma / np.sqrt(1 - rho**2)
+    y_sd = σ / np.sqrt(1 - ρ**2)
 
     # yhat
-    yhat = rho * y[:-1]
-    y_data = pmc.Normal('y_obs', mu=yhat, sigma=sigma, observed=y[1:])
+    yhat = ρ * y[:-1]
+    y_data = pmc.Normal('y_obs', mu=yhat, sigma=σ, observed=y[1:])
     y0_data = pmc.Normal('y0_obs', mu=0., sigma=y_sd, observed=y[0])
 ```
 
@@ -284,15 +296,14 @@ Please note how the posterior for $\rho$ has shifted to the right relative to wh
 Think about why this happens.
 
 ```{hint}
-It is connected to how Bayes Law (conditional probability) solves an **inverse problem** by putting high probability on parameter values
-that make observations more likely.
+It is connected to how Bayes Law (conditional probability) solves an **inverse problem** by putting high probability on parameter values that make observations more likely.
 ```
 
 We'll return to this issue after we use `numpyro` to compute posteriors under our two alternative assumptions about the distribution of $y_0$.
 
 We'll now repeat the calculations using `numpyro`.
 
-## Numpyro Implementation
+## Numpyro implementation
 
 ```{code-cell} ipython3
 
@@ -326,14 +337,14 @@ def plot_posterior(sample):
 ```{code-cell} ipython3
 def AR1_model(data):
     # set prior
-    rho = numpyro.sample('rho', dist.Uniform(low=-1., high=1.))
-    sigma = numpyro.sample('sigma', dist.HalfNormal(scale=np.sqrt(10)))
+    ρ = numpyro.sample('rho', dist.Uniform(low=-1., high=1.))
+    σ = numpyro.sample('sigma', dist.HalfNormal(scale=np.sqrt(10)))
 
     # Expected value of y at the next period (rho * y)
-    yhat = rho * data[:-1]
+    yhat = ρ * data[:-1]
 
     # Likelihood of the actual realization.
-    y_data = numpyro.sample('y_obs', dist.Normal(loc=yhat, scale=sigma), obs=data[1:])
+    y_data = numpyro.sample('y_obs', dist.Normal(loc=yhat, scale=σ), obs=data[1:])
 
 ```
 
@@ -370,17 +381,17 @@ Here's the new code to achieve this.
 ```{code-cell} ipython3
 def AR1_model_y0(data):
     # Set prior
-    rho = numpyro.sample('rho', dist.Uniform(low=-1., high=1.))
-    sigma = numpyro.sample('sigma', dist.HalfNormal(scale=np.sqrt(10)))
+    ρ = numpyro.sample('rho', dist.Uniform(low=-1., high=1.))
+    σ = numpyro.sample('sigma', dist.HalfNormal(scale=np.sqrt(10)))
 
     # Standard deviation of ergodic y
-    y_sd = sigma / jnp.sqrt(1 - rho**2)
+    y_sd = σ / jnp.sqrt(1 - ρ**2)
 
     # Expected value of y at the next period (rho * y)
-    yhat = rho * data[:-1]
+    yhat = ρ * data[:-1]
 
     # Likelihood of the actual realization.
-    y_data = numpyro.sample('y_obs', dist.Normal(loc=yhat, scale=sigma), obs=data[1:])
+    y_data = numpyro.sample('y_obs', dist.Normal(loc=yhat, scale=σ), obs=data[1:])
     y0_data = numpyro.sample('y0_obs', dist.Normal(loc=0., scale=y_sd), obs=data[0])
 ```
 
@@ -408,8 +419,7 @@ mcmc2.print_summary()
 
 Look what happened to the posterior!
 
-It has moved far from the true values of the parameters used to generate the data because of how Bayes' Law (i.e., conditional probability)
-is telling `numpyro` to explain what it interprets as  "explosive" observations early in the sample.
+It has moved far from the true values of the parameters used to generate the data because of how Bayes' Law (i.e., conditional probability) is telling `numpyro` to explain what it interprets as  "explosive" observations early in the sample.
 
 Bayes' Law is able to generate a plausible likelihood for the first observation by driving $\rho \rightarrow 1$ and $\sigma \uparrow$ in order to raise the variance of the stationary distribution.