According to Bayes’ Theorem , the probability of event A, given that event B has occurred, is P ( A | B ) = P ( A ) ⋅ P ( B | A ) P ( A ) ⋅ P ( B | A ) + P ( A ′ ) ⋅ P ( B | A ′ ) . In Exercises 33–38, use Bayes’ Theorem to find P ( A | B ). 36. P ( A ) = 0.62, P ( A ′) = 0.38, P ( B | A ) = 0.41, and P ( B | A ′) = 0.17
According to Bayes’ Theorem , the probability of event A, given that event B has occurred, is P ( A | B ) = P ( A ) ⋅ P ( B | A ) P ( A ) ⋅ P ( B | A ) + P ( A ′ ) ⋅ P ( B | A ′ ) . In Exercises 33–38, use Bayes’ Theorem to find P ( A | B ). 36. P ( A ) = 0.62, P ( A ′) = 0.38, P ( B | A ) = 0.41, and P ( B | A ′) = 0.17
Solution Summary: The author explains that the value of P(A|B) using Bayes' Theorem is 0.797.
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.
Introduction: MARKOV PROCESS And MARKOV CHAINS // Short Lecture // Linear Algebra; Author: AfterMath;https://www.youtube.com/watch?v=qK-PUTuUSpw;License: Standard Youtube License
Stochastic process and Markov Chain Model | Transition Probability Matrix (TPM); Author: Dr. Harish Garg;https://www.youtube.com/watch?v=sb4jo4P4ZLI;License: Standard YouTube License, CC-BY