fix typos

optimization/05_portfolio_optimization.py

@@ -47,7 +47,7 @@ def _(mo):
         r"""
         ## Asset returns and risk
 
-        We will only model investments held for one period. The initial prices are $p_i > 0$. The end of period prices are $p_i^+ >0$. The asset (fractional) returns are $r_i = (p_i^+-p_i)/p_i$. The porfolio (fractional) return is $R = r^Tw$.
+        We will only model investments held for one period. The initial prices are $p_i > 0$. The end of period prices are $p_i^+ >0$. The asset (fractional) returns are $r_i = (p_i^+-p_i)/p_i$. The portfolio (fractional) return is $R = r^Tw$.
 
         A common model is that $r$ is a random variable with mean ${\bf E}r = \mu$ and covariance ${\bf E{(r-\mu)(r-\mu)^T}} = \Sigma$.
         It follows that $R$ is a random variable with ${\bf E}R = \mu^T w$ and ${\bf var}(R) = w^T\Sigma w$. In real-world applications, $\mu$ and $\Sigma$ are estimated from data and models, and $w$ is chosen using a library like CVXPY.

probability/08_bayes_theorem.py

@@ -307,7 +307,7 @@ def _(mo):
     mo.md(
         r"""
 
-        _This interactive exmaple was made with [marimo](https://github.com/marimo-team/marimo/blob/main/examples/misc/bayes_theorem.py), and is [based on an explanation of Bayes' Theorem by Grant Sanderson](https://www.youtube.com/watch?v=HZGCoVF3YvM&list=PLzq7odmtfKQw2KIbQq0rzWrqgifHKkPG1&index=1&t=3s)_.
+        _This interactive example was made with [marimo](https://github.com/marimo-team/marimo/blob/main/examples/misc/bayes_theorem.py), and is [based on an explanation of Bayes' Theorem by Grant Sanderson](https://www.youtube.com/watch?v=HZGCoVF3YvM&list=PLzq7odmtfKQw2KIbQq0rzWrqgifHKkPG1&index=1&t=3s)_.
 
         Bayes theorem provides a convenient way to calculate the probability
         of a hypothesis event $H$ given evidence $E$: