Probabilistic Reasoning in Artificial Intelligence
Probabilistic reasoning is a type of knowledge where we apply the rule of probability to mark the degree of uncertainty. It gives the user a reason for any outcome by giving them probabilities. Using these probabilities, we can predict the happening of any events. The probabilistic models are used to test the data with the help of statical codes in AI.
Example
If you observe then, you will able to see the use of probability in your daily life too. The examples where we use the concepts of probability are the winning chances or percentages between two Cricket teams. It can also be a chance of winning a political party in an election or a probability of winning a lottery prize.
We can calculate the probability of a specific event with the help of the given formula.
P(A | B) = P(A ∩ B) / P(B)
Let us look at a problem statement, like how we can use the above formula in a question.
Q. In a group of friends, there are 60% of the boys love to play Cricket, and 50% of the boys like Cricket and Football. Now find the percentage of boys who like both Cricket and Football.
Sol. Suppose A is an event where the boys love Football
And B is an event where the boys love Cricket.
Now according to the above formula,
P(A | B) = P(A ∩ B) / P(B)
= 0.5 / 0.6
= 83%
Hence the probability of boys who like both Cricket and Football is 83%.
Need of Probabilistic Reasoning
The need for Probabilistic Reasoning in Artificial Intelligence includes the below points.
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AI needs it because uncertainty is inherited in real-world cases.
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When there is an unknown error comes during an experiment.
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When the prediction becomes too large, that can not be handled.
- When you get the unpredicted outputs.
Working of Probabilistic Reasoning
The working of Probabilistic Reasoning in Artificial Intelligence includes the below steps.
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At first, the model of probabilistic reads the learning data on which the prediction is needed.
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The framework helps the model to perform the steps to express and deploy the reservations.
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After the above steps, only the prediction part is left, which has to be done by the model.
- The model predicts the result with uncertainty.
Types of Probabilistic Reasoning
The main reason for probabilistic reasoning to come into existence is Uncertainty. Uncertainty can be defined as defects in various factors, like experimental errors, faults in tools, less data, and many more.
To resolve the issue of uncertainty, there are two ways. Let us have a look at them one by one.
Bayes' Rule
Bayes' rule is known as the basic rule of probability. It helps the model to update itself along with the prior knowledge and change the new evidence. We use the Bayes' Rule in many fields nowadays, like for prediction, classification, and decision-making jobs where uncertainty needs to be handled.
The mathematical formula of Bayes' Rule is as follows.
P(A|B) = (P(B|A) * P(A)) / P(B)
Here,
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The P(A|B) is posterior probability. This is the probability of occurrence of A event, given that B has occurred.
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P(B|A) is the same thing as P(A|B). This is the probability of occurrence of B event, given that B.
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The P(A) is the natural occurrence before taking any new evidence.
- The P(B) is the natural occurrence before taking any new evidence.
Bayesian Statistics
Bayesian statistics is a type of statistics that is used in probabilistic reasoning to analyze data. It offers a framework for the statistic and guess the probability on the basis of the data. It can be used in many places, such as social science, medicine, environment, and many more.
We can handle the Bayesian statistics in the different ways that are mentioned below. Let us look at them one by one in brief.
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Bayesian Updating: In this system, the system applies both rules to update the probabilities in different tests, which are based on prior rules and situations.
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Decision Making: Here, the system takes the complete probabilities posterior to make the final probability of the test taken.
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Likelihoods: In this process, the tests use statistical models to get an idea of likelihood behaviors, and the test taken gives a positive response only.
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Prior Probabilities: This is a process where the system gives the probability to different possible tests based on the data's preference.
Application of Probabilistic Reasoning
The application of probabilistic reasoning includes the following.
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Robotics: The use of AI and ML are the most vital keys in robotics. Hence we can use probabilistic reasoning in robotics.
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Engineering: It is very useful from the engineers' point of view as it helps to predict complex systems such as nuclear power plants, aircraft or bridges.
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Medicine: It is used in the medical field to predict diagnosis and treatment planning.
- Finance: It is used in finance to handle risk management, where it predicts stock market fluctuations, credit risks, and changes in interest rates.
Frequently Asked Questions
How many types of Probabilistic Reasoning in Artificial Intelligence are present?
There are mainly two types present which are the Bayes rule and the Bayesian statistics. The Bayes rule helps the model to update itself along with the prior knowledge and change the new evidence. Bayesian statistics are used to analyze data.
Why do we need Probabilistic Reasoning?
We need Probabilistic Reasoning in Artificial Intelligence because of many reasons like it helps to handle the uncertainty and to handle the unknown error that comes during the experiments.
Give an example where we can use probabilistic reasoning.
We can use probabilistic Reasoning in many places, like medicine, finance, robotics, engineering and Natural language processing.
Conclusion
This article discusses the topic of Probabilistic Reasoning in Artificial Intelligence. In detail, we have seen the definition, need, working, types, and applications with proper explanation.
We hope this blog has helped you enhance your knowledge of Probabilistic Reasoning in Artificial Intelligence. If you want to learn more, then check out our articles.
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