Bayes' Theorem of Probability With Tree Diagrams & Venn Diagrams
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- Опубликовано: 29 июн 2024
- 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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best explanation of Bayes' Theorem on RUclips by far
Yess
Yes
I have never seen such an easier explanation or believed that Bayes theorem is so simple. Can not thank you enough!
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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Wow, terrific. I have watched a lot of videos on Bayes Theorem and this is by far the best one. Thank you, JG!
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Best illustration of what Bayes Theorem is - dealing with 2 probabilities in series. Thanks a lot.
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Wow that second method where you create a tree diagram based off of an actual population estimate was really cleaver! Thanks for sharing.
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what i learned from this:
if you have cancer, relax, just use the conditional probabilities and see that its all just a probability, that will cure your depression for sure
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Amazing video. This theorem definitely required me to attempt a practice problem to properly understand it.
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This is the most elegant explanation of Bayes Theorem I have ever seen. Thank you sir.
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thanks for this one.. Bayes Rule had me really confused when trying to learn from the book.. first road block in Statistics and Probability
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