Formally, probability is defined via areas. The basic idea is that the probability of picking an element from a set A out of a set B is the ratio of the areas of A to B, where “area” can be defined not only for things like squares but also things like lines, or actually almost every* subset of R. So, lets say you want to randomly select a real number from the interval [0,1] and want to know the odds it falls in a set, S. The area of [0,1] is 1, so the answer is just the area of S.
If S={0}, then S has area zero. If S=[0,1), then S has area 1. Not only are both of these theoretical possibilities, they are practical ones too. There are real world examples of probability zero events (the only one that comes to mind involves QM though so I don’t want to bother with the details).
Now, notice that this isn’t the same thing as “impossible”. Instead, it means more like “it won’t happen I promise even by the time the universe ends”. The way I tend to think about probability zero events is that they are so unlikely they are beyond the reach of the principle that as the number of trials increases, events become expected. For any nonzero probability, there is a number of trials, n, such that once you do it n times the expected value becomes greater than 1. That’s not the case with probability zero events. Probability 1 events can then be thought of as the negation of probability 0 events.
*not actually “almost every” in a formal sense, but “almost any” in a “unless you go try to build a set that you can’t measure it probably has a well defined area” sense
That seems a solid enough explanation, but how can something of probability zero have a chance to occur? How then do you represent an impossible outcome? It seems like otherwise ‘zero’ is equivalent to ‘absurdly low’. That doesn’t quite jive with my understanding.
Impossible things also have a probability of zero. I totally understand that this seems a bit unintuitive, and the underlying structure (which includes things like infinities of different sizes) is generally pretty unintuitive at first. Which is kinda just saying “sorry, I can’t explain the intuition,” which is unfortunately true.
I’m just going to think of it as taking the limit as evidence approaches infinity. Because a probability next to zero and zero are identical, zero then is a probability?
I think one of the clearest expositions on these issues is ET Jaynes. The first three chapters (which is some of the relevant part) can be found at http://bayes.wustl.edu/etj/prob/book.pdf.
Formally, probability is defined via areas. The basic idea is that the probability of picking an element from a set A out of a set B is the ratio of the areas of A to B, where “area” can be defined not only for things like squares but also things like lines, or actually almost every* subset of R. So, lets say you want to randomly select a real number from the interval [0,1] and want to know the odds it falls in a set, S. The area of [0,1] is 1, so the answer is just the area of S.
If S={0}, then S has area zero. If S=[0,1), then S has area 1. Not only are both of these theoretical possibilities, they are practical ones too. There are real world examples of probability zero events (the only one that comes to mind involves QM though so I don’t want to bother with the details).
Now, notice that this isn’t the same thing as “impossible”. Instead, it means more like “it won’t happen I promise even by the time the universe ends”. The way I tend to think about probability zero events is that they are so unlikely they are beyond the reach of the principle that as the number of trials increases, events become expected. For any nonzero probability, there is a number of trials, n, such that once you do it n times the expected value becomes greater than 1. That’s not the case with probability zero events. Probability 1 events can then be thought of as the negation of probability 0 events.
*not actually “almost every” in a formal sense, but “almost any” in a “unless you go try to build a set that you can’t measure it probably has a well defined area” sense
That seems a solid enough explanation, but how can something of probability zero have a chance to occur? How then do you represent an impossible outcome? It seems like otherwise ‘zero’ is equivalent to ‘absurdly low’. That doesn’t quite jive with my understanding.
Impossible things also have a probability of zero. I totally understand that this seems a bit unintuitive, and the underlying structure (which includes things like infinities of different sizes) is generally pretty unintuitive at first. Which is kinda just saying “sorry, I can’t explain the intuition,” which is unfortunately true.
I’m just going to think of it as taking the limit as evidence approaches infinity. Because a probability next to zero and zero are identical, zero then is a probability?
I think one of the clearest expositions on these issues is ET Jaynes. The first three chapters (which is some of the relevant part) can be found at http://bayes.wustl.edu/etj/prob/book.pdf.
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Fixed Jaynes link (no trailing period).
Oops. Thanks for the fix!
Ah. Thanks!