Understanding Bayes’ Theorem
Disclaimer: I am writing this because I finally understood how Bayes’ Theorem works. Therefore, this is much more a reference note to myself than anything else, but I am publishing the information here because I think it might be useful for other people. I am no math expert (as can be deduced from the fact that I only understood the theorem now, after having seen it many times), so please let me know if you find any mistakes in my explanation.
Bayes’ Theorem states that
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where P(B|A) is the probability of event B occurring, given event A occurred; P(A|B) is the probability of event A occurring, given event B occurred; P(B) is the probability of only event B occurring; and P(A) is the probability of only event A occurring.
I have seen, and even used, this formula a number of times in my life, but I had never really understood what it was saying. But before I explain it, I will introduce a few basic concepts.
Relative frequency
Given an experiment with two, non-mutually exclusive possible outcomes, A and B, and n repetitions of that experiment, and let n1 be the number of occurrences of event A alone, n2 the number of occurrences of event B alone and n3 the number of occurrences of events A and B simultaneosly, the relative frequency of the occurrence of event A, or probability P(A) of event A, is given by
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where nA is the number of times event A occurred. Likewise, the relative frequency of the occurrence of event B, or probability P(B) of event B, is given by
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where nB is the number of times event B occurred. Finally, the relative frequency of the occurrence of both events, or probability P(AB) of events A and B, is given by
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Conditional probability
The relative frequency of event A occurring, given event B occurred, is given by
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Notice that in the denominator, we account for the occurrences of event B alone and of event B alongside with event A. The lower the value of nB (recall nB is the relative frequency of event B occurring alone), the higher the ratio of the equation above will be (yelding 1 when nB is zero, i.e., event B occurs only when event A occurs). If B occurs more often alongside with A than alone, then the probability of A occurring when B occurs will be higher.
Likewise,
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nA|B and nB|A may also be denoted P(A|B) and P(B|A), respectively. P(A|B) is read as “the probability of event A, given B”.
Given the above equations, we observe that
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or
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Now we’re ready to understand Bayes’ Theorem
Bayes’ Theorem
As mentioned in the beggining of this post, Bayes’ Theorem states that:
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Now, here’s how you should interpret it. A better way to visualize it is to write it as
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given the last equation from the previous section. The explanation is similar to the one given for the equation for nA|B in the previous section. If P(A) is close to P(AB) (recall P(A) is the probability of event A alone or alongside with event B), that means most occurrences of event A happen when event B also occurs. From that, we can intuitively conclude that event B will very likely occur given event A occured, since the occurrence of event A is strongly related to the occurrence of both A and B.
The explanation above can be expressed in terms of very informal (but I believe reasonable) logical statements:
1. A and B may occur
2. A tends to occur only when B also occurs
3. A occurred
4. It’s likely B will also occur
Note: this explanation is strongly based on the online tutorials for Digital Image Processing, by Gonzales & Woods. I used the same notation and terms. However, my objective here was to add the clarifying (at least for me!) explanations.

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