For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability.
For building a Bayes model, typically two main terms are required:
- Prior Probability: This represents our initial belief about the probability of an event occurring before we have observed any evidence. It is denoted as P(A), where A is the event.
- Likelihood: This represents the probability of observing the evidence given that the event has occurred. It is denoted as P(E|A), where E is the evidence and A is the event.
These two terms are fundamental components of Bayes’ theorem, which is used to update our beliefs about the probability of an event occurring based on observed evidence. So, in summary, two terms are required for building a Bayes model: the prior probability and the likelihood.