Artificial Intelligence: Probabilistic Reasoning

Following are some leading causes of uncertainty to occur in the real world.

Information occurred from unreliable sources.
Experimental Errors
Equipment fault
Temperature variation
Climate change.

Probabilistic reasoning:

Probabilistic reasoning is a way of knowledge representation where we apply the concept of probability to indicate the uncertainty in knowledge. In probabilistic reasoning, we combine probability theory with logic to handle the uncertainty.

Need of probabilistic reasoning in AI:

When there are unpredictable outcomes.
When specifications or possibilities of predicates becomes too large to handle.
When an unknown error occurs during an experiment.

In probabilistic reasoning, there are two ways to solve problems with uncertain knowledge:

Bayes' rule
Bayesian Statistics

Probability: Probability can be defined as a chance that an uncertain event will occur. It is the numerical measure of the likelihood that an event will occur. The value of probability always remains between 0 and 1 that represent ideal uncertainties.

0 ≤ P(A) ≤ 1, where P(A) is the probability of an event A.

P(A) = 0, indicates total uncertainty in an event A.

P(A) =1, indicates total certainty in an event A.

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