When classifying documents with naive Bayes, which probabilities do you multiply in case of repeated terms?
You could use either of the two approaches:


Just count whether your documents in your corpus contain or not a word $w$ and then estimate the likelihoods of $P(w_1|C)...P(w_n|C)$, as you describe
You could count how many instances of any word has each of the documents of your corpus, but then the estimation of the likelihoods becomes a bit more complicated than what you describe; in essence you need to use a multinomial distribution: $P(w_1|C=k) = \sum_{i=1}^{N}x_{it}z_{ik}/\sum_{s=1}^{|V|}\sum_{i=1}^{N}x_{is}z_{ik}$, where $N$ is the number of documents, $x_{it}$ in the numerator is the counts of word $w_1$ per document, $s$ in the denominator iterates the number of terms $|V|$ in your corpus and $x_{is}$ is the number times each term appears in each document, and finally $z_{ik}$ is 1 if the document belongs to class k, 0 otherwise, in both numerator in the denominator


Hopefully the short explanation makes sense, anyway you have a very complete and useful reference here (the same notation in the formula above).