The product rule and Bayes rule

In summary, Bayes rule is an axiom for probability that is used to derive Bayes theorem. It states that the probability of A and B occurring together is equal to the probability of A occurring given B, multiplied by the probability of B occurring. This principle is also intuitively illustrated in an example of drawing beads from a box. The derivation of Bayes theorem is based on the definition of conditional probability and shows that the probability of A given B is equal to the probability of B given A, multiplied by the probability of A, divided by the probability of B. This can be seen in the example of drawing beads with unique numbers written on them. While conditional probability can be complex, the main ideas can be understood through set-theory arguments
  • #1
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Is the rule:

P(AB I) = P(BA I)

(which is used to derive Bayes rule) an axiom for probability? And if so, do you guys find it intuitive that it should hold. For instance consider a box with green and red beads. Do you think it is strictly obvious that the probability of getting red-green is the same as the probability for getting green-red?

My question is because I do not think that Bayes rule is intuitive yet it is derived from exactly this principle.
 
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  • #2
I don't understand your notation. Regarding your example, I don't think it's at all obvious. I didn't even know that it was true. (I compared the probability of "red, then green" to the probability of "green, then red" in a few simple examples, and they were equal every time, so it seems to be true).

But I don't see what it has to do with Bayes theorem. The following example seems more appropriate: Suppose that the total number of beads in your example is n, and that each of them has a unique integer between 1 and n written on it. Now the probability that the first bead you draw "is red and has an odd number" is the same as the probability that the first bead you draw "has an odd number and is red". That is intuitively obvious.

I have never taken a course on probability, so maybe I'm missing something simple, but isn't the derivation of Bayes theorem just what I'm doing below?

We define P(A|B) by
$$P(A|B)=\frac{P(A\cap B)}{P(B)}.$$ This equality implies that $$P(A\cap B)=P(A|B)P(B).$$ Since A and B are arbitrary here, we also have $$P(B\cap A)=P(B|A)P(A).$$ Since the two left-hand sides are equal (by definition of ##\cap##), the two right-hand sides must be equal. This implies that
$$P(A|B)=\frac{P(B|A)P(A)}{P(B)}.$$
 
  • #3
Fredrik said:
I have never taken a course on probability, so maybe I'm missing something simple, but isn't the derivation of Bayes theorem just what I'm doing below?
That looks right to me. In practice, conditional probability can become really confusing, especially when ##A## and ##B## are uncountable and ##P## is some kind of integral. But the main ideas follow from the set-theory argument you wrote. (Also, it only works if ##P(B)\neq0##, otherwise we just divided by zero.)
 

Related to The product rule and Bayes rule

1. What is the product rule in probability?

The product rule, also known as the multiplication rule, is a fundamental rule of probability that states the probability of two independent events occurring together is equal to the product of their individual probabilities.

2. How is the product rule used in Bayes rule?

In Bayes rule, the product rule is used to calculate the probability of a hypothesis given new evidence, by multiplying the prior probability of the hypothesis with the likelihood of the evidence given the hypothesis.

3. What is the difference between the product rule and Bayes rule?

The product rule is a general rule of probability that applies to all independent events, while Bayes rule is a specific application of the product rule in which new evidence is used to update the probability of a hypothesis.

4. Can you provide an example of how Bayes rule is used in real life?

One example of Bayes rule in real life is in medical diagnostics. Given a certain symptom, Bayes rule can be used to update the probability of a patient having a specific disease based on the prior probability of the disease and the likelihood of the symptom occurring in patients with and without the disease.

5. How does Bayes rule incorporate prior knowledge?

Bayes rule incorporates prior knowledge by using the prior probability of a hypothesis, which is based on past observations or information, to update the probability of the hypothesis when new evidence is introduced.

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