When to Use Total Probability Formula and Bayes Formula?

In summary, when solving problems involving probabilities, it is important to carefully examine the given information and use the appropriate formula, such as the total probability formula or Bayes formula, to find the desired probability. There is no definitive rule for when to use which formula, as it depends on the given information and the desired probability. With practice, one can become familiar with different probability theorems and determine which to use in a given situation.
  • #1
philipSun
9
0
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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  • #2
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).
 

Related to When to Use Total Probability Formula and Bayes Formula?

1. What is Bayes Theorem probability?

Bayes Theorem probability is a mathematical concept that helps calculate the probability of an event occurring based on prior knowledge or information. It is named after Thomas Bayes, an 18th-century mathematician, and is widely used in fields such as statistics, economics, and machine learning.

2. How does Bayes Theorem work?

Bayes Theorem uses conditional probabilities to calculate the likelihood of an event occurring. It takes into account both the prior probability of an event and new evidence that may affect the probability. The formula for Bayes Theorem is P(A|B) = (P(B|A) * P(A)) / P(B), where A and B are events and P(A|B) is the probability of A given B has occurred.

3. What are the applications of Bayes Theorem probability?

Bayes Theorem probability has various applications in different fields, such as in medical diagnosis, spam filtering, and weather forecasting. It is also commonly used in decision-making processes and risk analysis.

4. What are the limitations of Bayes Theorem probability?

Bayes Theorem probability relies heavily on the accuracy of the prior knowledge or information used. If the prior information is inaccurate or incomplete, the calculated probability may also be incorrect. Additionally, Bayes Theorem assumes that events are independent of each other, which may not always be the case in real-world situations.

5. How is Bayes Theorem different from other probability theories?

Bayes Theorem differs from other probability theories in that it takes into account prior information when calculating the probability of an event. This allows for a more accurate calculation, especially when new evidence or information is introduced. Other probability theories, such as the classical and frequentist approaches, do not consider prior knowledge in their calculations.

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