Bayes' Theorem of Probability With Tree Diagrams & Venn Diagrams

This video tutorial provides an intro into Bayes' Theorem of probability. It explains how to use the formula in solving example problems in addition to using tree diagrams and venn diagrams.
Introduction to Probability: • Introduction to Probab...
Probability Formulas:
• Probability Formulas, ...
Probability Explained:
• Probability Explained!
Probability With Geometry:
• Probability With Geome...
Probability of Complementary Events:
• Probability of Complem...
Conditional Probability:
• Conditional Probabilit...
__________________________________
Independent and Dependent Events:
• Probability - Independ...
Probability of Mutual Exclusive Events:
• Probability of Mutuall...
Multiplication and Addition Rule:
• Multiplication & Addit...
Compound Probability:
• Compound Probability o...
Expected Value:
• How To Calculate Expec...
Probability Tree Diagrams:
• Probability Tree Diagrams
___________________________________
Bayes Theorem:
• Bayes' Theorem of Prob...
Probability - Binomial Distribution:
• Finding The Probabilit...
Probability - Geometric Distribution:
• Geometric Distribution...
Probability - Poisson Distribution:
• Introduction to Poisso...
Continuous Probability Distributions:
• Continuous Probability...
Probability Density Functions:
• Cumulative Distributio...
__________________________________
Probability - Uniform Distributions:
• Continuous Probability...
Probability - Exponential Distributions:
• Probability Exponentia...
Probability - Normal Distributions (Calculus):
• Normal Distribution & ...
Probability - Standard Normal Distributions:
• Standard Normal Distri...
Probability - The Law of Large Numbers:
• Law of Large Numbers
___________________________________
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www.video-tutor.net/
Full-Length Videos and Worksheets:
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[@TheOrganicChemistryTutor]

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I just realized something: P(A|B) = P(A & B)/P(B) is the same as P(A & B) * 1/P(B). This might seem trivial, but consider what the inverse of probability is! It's a "rate of occurrence", or something like that. Because you already know event B happens, it only makes sense that we need to use the inverse of probability to properly scale the probability P(A & B). Dividing by a probability "increases" the conditional probability, which is what you would expect given that event B is known to have happened. Consider that the P(B) is very low, like 1/1000. If event B known to happen, that makes P(A|B) 1000 x more likely than P(A&B), which is 1/P(B). When I realized this, the concept seems to sink in easier. Thanks for this video that helped me make the connection!

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