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.
[or]
Prior Probability (P(h)): Probability before observing any evidence
Bayes Theorem:
Classification Using Bayes Model
- Bayes theorem helps choose the most probable hypothesis from a set of hypotheses.
- It forms the basis for Naive Bayes Classifier.
