4. Conditional Probability
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.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
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
- -- Conditional Probability
- The probability of an event A occurring given that another event B has occurred.
- -- Independent Events
- Two events A and B are independent if the occurrence of one does not affect the probability of the other.
- -- Mutually Exclusive Events
- Events that cannot occur simultaneously.
- -- Bayes’ Theorem
- A formula used to update the probability estimate for a hypothesis as additional relevant evidence is acquired.
- -- Total Probability Theorem
- A formula used to calculate the total probability of an event based on its partition into smaller, mutually exclusive events.
Additional Learning Materials
Supplementary resources to enhance your learning experience.