When to Use Total Probability Formula and Bayes Formula?

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SUMMARY

The discussion clarifies the application of the Total Probability Formula and Bayes' Theorem in probability theory. The Total Probability Formula is utilized when dealing with non-conditional probabilities to derive conditional probabilities, while Bayes' Theorem is applied when conditional probabilities are known and need to be updated based on new evidence. The key takeaway is that the choice between these formulas depends on the type of information available, whether it is conditional or non-conditional.

PREREQUISITES
  • Understanding of conditional and non-conditional probabilities
  • Familiarity with Bayes' Theorem
  • Basic knowledge of probability notation (e.g., P(A|B), P(A and B))
  • Experience with solving probability problems
NEXT STEPS
  • Study the derivation and applications of Bayes' Theorem
  • Practice problems involving the Total Probability Formula
  • Explore examples of conditional versus non-conditional probabilities
  • Learn about common pitfalls in applying probability theorems
USEFUL FOR

Students, mathematicians, data scientists, and anyone interested in mastering probability theory and its applications in real-world scenarios.

philipSun
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Hello.

I know that total probability formula is in Bayes formula/Theorem. But how do I know when I must use
- total probability formula ?
- Bayes formula?

I want to know what is difference between total probability formula and Bayes formula/Theorem.

I don't know when total probability formula is ( Bayes formula is not needed ) enough?
 
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philipSun said:
Hello.

I know that total probability formula is in Bayes formula/Theorem. But how do I know when I must use
- total probability formula ?
- Bayes formula?

I want to know what is difference between total probability formula and Bayes formula/Theorem.

I don't know when total probability formula is ( Bayes formula is not needed ) enough?

Hello philipSun and welcome to the forums.

One guiding principle that you should use when solving any problem (especially mathematical, scientific and so on), is to look at the information you are given.

Just list all the information you have in mathematical language and then see if you can transform it to something that you need.

If you are given conditional information, then you might have to use that to get some other probability, or if you are given non-conditional probability, then you might have to use that to get conditional probability and so on.

There is no straight up final answer to your question. Basically a lot of mathematics is about representing the same thing in a few different ways and the probability theorems are no different.

After you do a few problems, you'll pretty much know what you have to find and what kinds of information you are given in terms of probabilities (like P(A|B = 1) = 0.2, P(A and B) = 0.13 and so on).
 

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