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:

Probability Formulas:

Probability Explained:

Probability With Geometry:

Probability of Complementary Events:

Conditional Probability:

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Independent and Dependent Events:

Probability of Mutual Exclusive Events:

Multiplication and Addition Rule:

Compound Probability:

Expected Value:

Probability Tree Diagrams:

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Bayes Theorem:

Probability - Binomial Distribution:

Probability - Geometric Distribution:

Probability - Poisson Distribution:

Continuous Probability Distributions:

Probability Density Functions:

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Probability - Uniform Distributions:

Probability - Exponential Distributions:

Probability - Normal Distributions (Calculus):

Probability - Standard Normal Distributions:

Probability - The Law of Large Numbers:

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Final Exams and Video Playlists:

Full-Length Videos and Worksheets:

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[debasismondal]

I have never seen such an easier explanation or believed that Bayes theorem is so simple. Can not thank you enough!

[sherif]

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[RayRay-ytpe]

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[jeromechua]

best explanation of Bayes' Theorem on Youtube by far

[camerongridley]

Wow, terrific. I have watched a lot of videos on Bayes Theorem and this is by far the best one. Thank you, JG!

[josephlam]

Best illustration of what Bayes Theorem is — dealing with 2 probabilities in series. Thanks a lot.

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[professorpanda]

Wow that second method where you create a tree diagram based off of an actual population estimate was really cleaver! Thanks for sharing.

[SciHeartJourney]

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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