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Knowledge
Conditional probability is essential in probability theory, particularly for applications in fields such as machine learning and engineering. The chapter covers conditional probability definitions, rules, and practical examples, emphasizing its importance in predictive modeling and decision-making. Key formulas like Bayes’ Theorem and Total Probability are discussed alongside real-world applications across various engineering disciplines.
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Sections
References
unit 3 ch4.pdfClass Notes
Memorization
What we have learnt
- Conditional probability hel...
- Key formulas include condit...
- Applications span across fi...
Final Test
Revision Tests
What we have learnt
- Conditional probability helps refine predictions based on new information.
- Key formulas include conditional probability, Bayes’ Theorem, and the Total Probability Theorem.
- Applications span across fields such as computer science, engineering, finance, and medicine.
Key Concepts
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Term: Conditional Probability
Definition: The probability of an event A occurring given that another event B has occurred.
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Term: Independent Events
Definition: Two events A and B are independent if the occurrence of one does not affect the probability of the other.
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Term: Mutually Exclusive Events
Definition: Events that cannot occur simultaneously.
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Term: Bayes’ Theorem
Definition: A formula used to update the probability estimate for a hypothesis as additional relevant evidence is acquired.
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Term: Total Probability Theorem
Definition: A formula used to calculate the total probability of an event based on its partition into smaller, mutually exclusive events.