Fix typo in index.md (Probability Review)

preliminaries/probabilityreview/index.md

@@ -88,7 +88,7 @@ More formally, define a mapping $$X : \Omega \to E$$ between two measurable spac
 
 **Example**: In our experiment above, suppose that $$X(\omega)$$ is the number of heads which occur in the sequence of tosses $$\omega$$. Given that only 10 coins are tossed, $$X(\omega)$$ can take only a finite number of values (0 through 10), so it is known as a discrete random variable. Here, the probability of the set associated with a random variable $$X$$ taking on some specific value $$k$$ is $$P(X = k) := P(\{\omega : X(\omega) = k\}) = P(\omega \in \text{all sequences with k heads})$$. Note that the set of all sequences with $$k$$ heads is an element of $$\mathcal{F}$$, given that $$\mathcal{F}$$ consists of all subsets of $$\Omega$$.
 
-**Example**: Suppose that $$X(\omega)$$ is a random variable indicating the amount of time it takes for a radioactive particle to decay ($$\omega$$ for this example could be some underlying characterization of the particle that changes as it decays). In this case, $$X(\omega)$$ takes on a infinite number of possible values, so it is called a continuous random variable. We denote the probability that $$X$$ takes on a value between two real constants $$a$$ and $$b$$ (where $$a < b$$) as $$P(a \leq X \leq b) := P(\{\omega : a \leq X(\omega) \leq b\})$$.
+**Example**: Suppose that $$X(\omega)$$ is a random variable indicating the amount of time it takes for a radioactive particle to decay ($$\omega$$ for this example could be some underlying characterization of the particle that changes as it decays). In this case, $$X(\omega)$$ takes on an infinite number of possible values, so it is called a continuous random variable. We denote the probability that $$X$$ takes on a value between two real constants $$a$$ and $$b$$ (where $$a < b$$) as $$P(a \leq X \leq b) := P(\{\omega : a \leq X(\omega) \leq b\})$$.
 
 When describing the event that a random variable takes on a certain value, we often use the **indicator function** $$\mathbf{1}\{A\}$$ which takes value 1 when event $$A$$ happens and 0 otherwise. For example, for a random variable $$X$$,