diff --git a/probability_theory/probability_theory.Rmd b/probability_theory/probability_theory.Rmd
index 63dc957..5b5850e 100644
--- a/probability_theory/probability_theory.Rmd
+++ b/probability_theory/probability_theory.Rmd
@@ -719,7 +719,7 @@ reparameterizations then translate from one equivalent manifestation to another.
 For discrete spaces all reparameterizations can be reduced to permutations that
 simply rearrange the individual elements of the space.  This simple intuition,
 however, doesn't carry over to continuous spaces for which reparameterizations
-can be signficantly more subtle.
+can be significantly more subtle.
 
 # Great Expectations {#sec:expectation_values}
 
@@ -930,7 +930,7 @@ $$
 \mathbb{m}_{\pi} = \mathbb{m}_{\pi, 1} = \mathbb{E}_{\pi} [ \iota ],
 $$
 which quantifies a sense of _where_ in the target space the probability 
-distribution is concentration its allocation.  
+distribution is concentrating its allocation.  
 
 At the same time the _variance_ of a probability distribution is the second 
 central moment,
@@ -1250,7 +1250,7 @@ poisson_prob(A1, l)
 which delightedly yield the same answer.
 
 Given this rousing success we can then ask what is the probability allocated to
-the compliment of $A_{1}$?
+the complement of $A_{1}$?
 ```{r}
 plot_poisson(l)
 plot_poisson_probs(0:(A1[1] - 1), l)
@@ -1583,7 +1583,7 @@ norm_prob <- function(B_min, B_max, mu, sigma) {
 norm_prob(B1_min, B1_max, mu, sigma)
 ```
 
-And the probability allocated to the compliment of $B_{1}$?
+And the probability allocated to the complement of $B_{1}$?
 ```{r}
 plot_norm(mu, sigma)
 plot_norm_probs(mu, sigma, -8, B1_min)
@@ -2095,7 +2095,7 @@ indicator <- function(x, A) {
 ```
 
 For example, to compute the probability of the set $A_{1}$ we evaluate the
-corresponding indictor function at each of our samples and then compute the
+corresponding indicator function at each of our samples and then compute the
 Monte Carlo estimator and standard error,
 ```{r}
 pushforward_samples = sapply(stan_samples, function(x) indicator(x, A1))