There's no such a thing as estimating parameters "through training data". To get point estimates, you can train a probabilistic model by [maximizing the likelihood function (MLE)][1] alone, or maximizing the posterior probabilities (MAP). The above description says that it uses MAP, so it considers not only the data but also a prior. In naive Bayes, the common choice is to use [Laplace smoothing][2] (uniform prior) to prevent probabilities of zeroes for the unobserved cases, which would zero-out everything in the calculations.


  [1]: https://stats.stackexchange.com/questions/112451/maximum-likelihood-estimation-mle-in-layman-terms
  [2]: https://stats.stackexchange.com/questions/525072/how-to-choose-prior-in-laplace-smoothing-naive-bayes/525166#525166