1. Bayes' theorem describes relationship between pre test and post test probability.

2. Bayesian inference is therefore just the process of deducing properties about a population or probability distribution from data using Bayes’ theorem

3. Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available

4. These data are necessary to estimate the relative a priori probability of possible carcinomas. Based on Bayes' theorem, the a priori probability can then be used to calculate the diagnostically relevant predictive values for immunostaining results with the chosen markers.

5. On the basis of sequential samples — employing Bayes theorem, laid down in short — the a-priori probabilities can be transformed into a-posteriori probabilities and are used for a new operational decision, that will now be termed as a continuation decision.

6. Bayesian definition is - being, relating to, or involving statistical methods that assign probabilities or distributions to events (such as rain tomorrow) or parameters (such as a population mean) based on experience or best guesses before experimentation and data collection and that apply Bayes' theorem to revise the probabilities and distributions after obtaining experimental data.

7. Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.; It is mainly used in text classification that includes a high-dimensional training dataset.; Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine