Hashing: Typos

06-hash/hash.tex

@@ -815,7 +815,7 @@ Bloom filters are a~family of data structures for approximate representation of
 in a~small amount of memory. A~Bloom filter starts with an empty set. Then it supports
 insertion of new elements and membership queries. Sometimes, the filter gives a~\em{false
 positive} answer: it answers {\csc yes} even though the element is not in the set.
-We will calculate the probability of false positves and decrease it at the expense of
+We will calculate the probability of false positives and decrease it at the expense of
 making the structure slightly larger. False negatives will never occur.
 
 \subsection{A trivial example}
@@ -900,7 +900,7 @@ If we set~$p$, it follows that $m \approx -n / \ln p$. Since all bands must fit
 of memory, we want to use $k = \lfloor M/m\rfloor \approx -M/n \cdot \ln p$ bands. False
 positives occur if we find~1 in all bands, which has probability
 $$
-	(1-p)^k \approx
+	(1-p)^k =
 	\e^{k\ln(1-p)} \approx
 	\e^{-M/n \cdot \ln p \cdot \ln(1-p)}.
 $$