7.a) Define Prior Probability. Explain Bayes Theorem, hML and hMAP with an example.
Answer:
Prior Probability:
It is the general probability of an uncertain event before an observation is seen or some evidence is
collected. It is the initial probability that is believed before any new information is collected.
Bayes Theorem:
Naive Bayes Model relies on Bayes theorem that works on the principle of three kinds of probabilities called prior probability, likelihood probability, and posterior probability.
Maximum A Posteriori (MAP) Hypothesis, hMAP
Maximum Likelihood (ML) Hypothesis, hML
Correctness of Bayes Theorem:
Example:
