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P.s. I appreciate that, unless you were Laplace’s demon, you couldn’t know this in advance of the spin but I’m still wonderin if, even though we would describe the probabilites as 18/37 and 19/37, they would actually be 1 and 0.

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P.s. I appreciate that, unless you were Laplace’s demon, you couldn’t know this in advance of the spin but I’m still wonderin if, even though we would describe the probabilites as 18/37 and 19/37, they would actually be 1 and 0.

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One is the “frequentists” which view probability similarly to how you are suggesting. Probabilities only exist as the fraction of results with a given outcome after an infinite number of trials. So probability is not defined for a single spin. You would have to spin an infinite number of times and get red each time for the probability to be 1.

The other is the “Bayesians” which take probability as a measure of our belief. For them probability of a single event is well defined, and can even be measured in terms of hypothetical bets. Even if the universe is truly deterministic, we may not know enough to determine the outcome, so the probability would account for our ignorance.

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I'm not sure if the determinism part is

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“One is the “frequentists”...”

One is the “frequentists” which view probability similarly to how you are suggesting. Probabilities only exist as the fraction of results with a given outcome after an infinite number of trials. So probability is not defined for a single spin. You would have to spin an infinite number of times and get red each time for the probability to be 1.

The other is the “Bayesians” which take probability as a measure of our belief. For them probability of a single event is well defined, and can even be measured in terms of hypothetical bets. Even if the universe is truly deterministic, we may not know enough to determine the outcome, so the probability would account for our ignorance.

Which of these two schools of thought dominate maths (I’m thinking of your average text book which tells us that the P of rolling a 6 on a die is 1/6 etc)?

“...after an infinite number of trials.”

Is this the idea that we will see exactly 18/37 reds and 19/37 non-reds over an infinite number of trials? Or exactly 50% heads, 50% tails etc?

“So probability is not defined for a single spin.”

So it’s not the frequentists who are writing your average text book?

“You would have to spin an infinite number of times and get red each time for the probability to be 1.”

Which can’t happen, even in theory, given that there’s 18 red pockets, 19 non-red pockets, yeah?

“For them (the Bayesians) probability of a single event is well defined, and can even be measured in terms of hypothetical bets.”

What would a Bayesian say about a single spin of the wheel regards red v non-red?

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Stephen Tashi

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We must distinguish between mathematics and the application of mathematics. The mathematics of probability theory makes no comment of how physical situations produce probabilities. It doesn't specify any relation between probabilities and the actual frequencies with which events occur. If a math textbook states something about the probability of rolling a 6 with a "fair" die, it is merely a hypothesis in a problem - in the same sense that a statement like "Ed has 6 apples" can be a hypothesis (a "given") in algebra problem. As far as the pure mathematics of probability theory goes, it is a special case of "measure theory". There is no distinct "frequentist measure theory" versus "Bayesian measure theory".Which of these two schools of thought dominate maths (I’m thinking of your average text book which tells us that the P of rolling a 6 on a die is 1/6 etc)?

It is in applications of probability theory that there is a distinction between the Bayesian and frequentist approaches. To get mathematical solutions to real life problems, assumptions must be made. The frequentist and Bayesian approaches differ in how they choose to make assumptions. When it comes to applying probability theory to physics, there are many different interpretations of probability. In fact, there are many different interpretations of probability among people who call themselves Bayesians.

Textbooks on probability theory often try to be helpful by mixing the mathematics of probability with examples of its application. For example, the typical text on introductory statistics teaches the "frequentist" approach to applying probability theory. Modern textbooks about probability theory (as opposed being only about the topic of statistics) usually show both the frequentist and Bayesian approaches.

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Stephen Tashi

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Of course, you mean theThere is no question that the physics of a roulette ball are completely deterministic

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Mark44

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I'm thinking that maybe you don't understand probability.P.s. I appreciate that, unless you were Laplace’s demon, you couldn’t know this in advance of the spin but I’m still wonderin if, even though we would describe the probabilites as 18/37 and 19/37, they would actually be 1 and 0.

This isn't a term that mathematicians use to describe themselves, to the best of my knowledge.“One is the “frequentists”...”

Are you questioning whether this probability is correct?Which of these two schools of thought dominate maths (I’m thinking of your average text book which tells us that the P of rolling a 6 on a die is 1/6 etc)?

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The frequentist school of thought is by far more common. Although both will give you the same p for rolling a fair die.Which of these two schools of thought dominate maths (I’m thinking of your average text book which tells us that the P of rolling a 6 on a die is 1/6 etc)?

Yes, assuming tautologically that it is a fair wheel or a fair coin.Is this the idea that we will see exactly 18/37 reds and 19/37 non-reds over an infinite number of trials? Or exactly 50% heads, 50% tails etc?

It is the frequentists, but the question you asked is not something that is addressed in standard courses.So it’s not the frequentists who are writing your average text book?

That depends on what prior information they have. If they know with complete certainty the determined outcome then they would say 1 or 0 accordingly. Otherwise they would take any limited information they have into account.What would a Bayesian say about a single spin of the wheel regards red v non-red?

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“...the use of probability reflects our ignorance of the outcome - i.e. in the absence of any relevant information, any particular spot on the wheel is as likely as any other.”

I appreciate that assuming it is deterministic (as you say it is) we are still ignorant of what the outcome will be; but I don’t see how “any particular spot on the wheel is as likely as any other” if it is deterministic. I can see why from our subjective point of view it would seem like that, given our ignorance of the outcome, but the objective reality would be that THERE IS ONLY ONE POSSIBLE OUTCOME, i.e, the ball will land in a red pocket because that is what has been determined by antecedent events. Which is why I’m wondering if the actual probability for red is 1, and the actual probability for non-red is 0.

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We must distinguish between mathematics and the application of mathematics. The mathematics of probability theory makes no comment of how physical situations produce probabilities. It doesn't specify any relation between probabilities and the actual frequencies with which events occur. If a math textbook states something about the probability of rolling a 6 with a "fair" die, it is merely a hypothesis in a problem - in the same sense that a statement like "Ed has 6 apples" can be a hypothesis (a "given") in algebra problem. As far as the pure mathematics of probability theory goes, it is a special case of "measure theory". There is no distinct "frequentist measure theory" versus "Bayesian measure theory".

It is in applications of probability theory that there is a distinction between the Bayesian and frequentist approaches. To get mathematical solutions to real life problems, assumptions must be made. The frequentist and Bayesian approaches differ in how they choose to make assumptions. When it comes to applying probability theory to physics, there are many different interpretations of probability. In fact, there are many different interpretations of probability among people who call themselves Bayesians.

Textbooks on probability theory often try to be helpful by mixing the mathematics of probability with examples of its application. For example, the typical text on introductory statistics teaches the "frequentist" approach to applying probability theory. Modern textbooks about probability theory (as opposed being only about the topic of statistics) usually show both the frequentist and Bayesian approaches.

Thanks, Stephen. I looked up measure theory but it’s too advanced for me at the moment. Can you tell me in the meantime if I’ve understood part of what you said correctly. Pure maths, regards probability, is not based on any number of observed events (e.g, looking at thousands of spins of a roulette wheel) it’s purely theoretical. We ASSUME that with an unbiased roulette wheel and an unbiased croupier the ball has an equal chance of landing in any of the pockets and assign a probability accordingly (the P for each pocket is 1/37)?

“The frequentist and Bayesian approaches differ in how they choose to make assumptions.”

Could you give me some examples of how they might differ in their assumptions regards my example of red v non-red?

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In the Bayesian approach probability does not reflect objective reality. In the frequentist approach it represents the objective reality of the proportion in an infinite ensemble of experiments. So a frequentist cannot assign a probability for a single event.the objective reality would be that THERE IS ONLY ONE POSSIBLE OUTCOME,

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If what you say was true, then (outside of quantum mechanics) the odds for anything would be either zero or one and statistics would be of absolutely no use to anyone If I flip a coin, look at the outcome and ask you to guess heads or tails, then, yes, the probability depends on your ignorance. Just accept probability as a measure of ignorance about a system and stop overthinking it, like it represents some objective reality.“...the use of probability reflects our ignorance of the outcome - i.e. in the absence of any relevant information, any particular spot on the wheel is as likely as any other.”

I appreciate that assuming it is deterministic (as you say it is) we are still ignorant of what the outcome will be; but I don’t see how “any particular spot on the wheel is as likely as any other” if it is deterministic. I can see why from our subjective point of view it would seem like that, given our ignorance of the outcome, but the objective reality would be that THERE IS ONLY ONE POSSIBLE OUTCOME, i.e, the ball will land in a red pocket because that is what has been determined by antecedent events. Which is why I’m wondering if the actual probability for red is 1, and the actual probability for non-red is 0.

And ignore the bayesian / frequentist stuff - its a huge waste of time going down that rabbit hole.

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I disagree completely. The source of his confusion is trying to put a frequentist interpretation of probability where it cannot be applied. The OP needs to be aware that there is an alternative interpretation where it can be applied but in that interpretation probability is not objective.And ignore the bayesian / frequentist stuff - its a huge waste of time going down that rabbit hole.

Which is the distinction between the frequentist and Bayesian interpretations of probability, in a nutshell.Just accept probability as a measure of ignorance about a system and stop overthinking it, like it represents some objective reality.

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Stephen Tashi

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One way to conceptualize measure theory is that it is an abstract treatment of the idea of "area" or "volume". A set (such as the possible outcomes of the toss of a fair coin, {H,T}) has certain subsets (such as {H},{T}) for which there is a function p() that assigns a "measure" to the subsets (such as p{H} = 1/2, p{T} = 1/2). No interpretation of what the measure means is given.Thanks, Stephen. I looked up measure theory but it’s too advanced for me at the moment.

Yes. The pure mathematical treatment of probability does not specify any physical experiments for measuring probabilities.Can you tell me in the meantime if I’ve understood part of what you said correctly. Pure maths, regards probability, is not based on any number of observed events (e.g, looking at thousands of spins of a roulette wheel) it’s purely theoretical.

I'll say no.We ASSUME that with an unbiased roulette wheel and an unbiased croupier the ball has an equal chance of landing in any of the pockets and assign a probability accordingly (the P for each pocket is 1/37)?

Making that statement seems to assume that "unbiased" is concept from physics and that "chance of" has either a physical definition or a mathematical definition that is more fundamental than the phrase "probability of". If you

We have to face the fact that a logically consistent concept of "probability" contradicts any attempts to make definite assertions that do not themselves involve the concept of "probability". Probability theory is circular in that respect. For example if the probability that a "fair" coin is tossed 100 times, we can say nothing definite about whether it lands heads 50 times or whether it lands heads between 40 and 60 times. We can only compute the

The circular nature of probability theory is unsatisfactory from the point of view of confronting real life problems. However the circular nature of probability theory is essential from the point of view of doing mathematics. Attempts to make probability theory say something definite about things other than probabilities lead to metaphysical disputes. (e.g. Can an event with probability zero happen? If I toss a fair coin long enough, will I (definitely) eventually get 3 times as many heads as tails? etc.) Mathematical probability theory wisely does not touch such questions!

Let say you have a roulette wheel not know to be "fair".Could you give me some examples of how they might differ in their assumptions regards my example of red v non-red?

A frequentist approach is to assume the probability of red is a some fixed, but unknown number ##P_{red}##. We assume the wheel is fair, i.e. we assume ##P_{red} = 18/37##. We compute the probability ##p## that in 3700 results from the wheel we would get between ##1800 - T## and ##1800 + T## reds. We choose ##T## so that ##p## is "small" (e.g. p = 0.05 is a typical choice). We observe 3700 outcomes of the wheel. If the number of reds is less than ##1800-T## or greater than ##1800 + T## we "reject" the hypothesis that the wheel is fair.

In the frequentist approach, the choice of ##T## is subjective. The statement that we "reject" the hypothesis is not the same as saying definitely "The wheel is not fair" or asserting that the probability the wheel is not fair is such-and-such. The frequentist approach is simply a

A Bayesian approach would be to imagine the wheel in question was taken at random from a population of wheels and this results in some probability distribution for the number ##P_{red}##. (In this example, you must swallow the notion of "a probability of a probability"). For example we might assume the ##P_{red}## is chosen from a uniform distribution of numbers over the interval [0.4, 0.7]. We observe 3700 outcomes of the wheel and observe ##N_{red}## reds. Then we compute the function that gives us the probability that ##P_{red} = x ## given there were ##N_{red}## reds. (Imagine making a graph of this function.) Making a decision about whether the wheel is fair or not is done on the basis of that function and other assumed or known information that quantifies the expected gain or loss from making a correct or incorrect decision.

A fundamental distinction between the frequentist and Bayesian approaches is that the frequentist procedure involves computing the probability of the data

The frequentist and Bayesian procedures also differ in what subjective assumptions must be made. The Bayesian approach requires more elaborate assumptions.

People can offer various philosophical reasons for using the above approaches (e.g. that ##P_{red}## is an objective property of the physical wheel versus that ##P_{red}## is quantification of our ignorance about whether a red happens etc.). The mathematical procedures themselves do not specify particular philosophies or metaphysics. However, it's fair to say that metaphysical views about probabilities being a measure of information or ignorance can be used to justify Bayesian procedures.

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18/37, as the 'fair' odds would be the mean of a distribution of 'unfair' wheels. As the wheel is spun an arbitrary number of times and the 'unfairness' begins to emerge, then the Bayesian would update the distribution accordingly.

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Stephen Tashi

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A Bayesian guess would be 18/37, but for a prior that can be updated, we'd need a distribution where other values are possible. (i.e. the prior distribution ##Pr(P_{red} = 18/37) = 1## can't be updated .)A simpler Bayesian approach would be to say, that in the absence of other information, how the wheel is 'unfair' is unknown, so the prior for red would simply be

18/37

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No, the P(red)=18/37 is the mean of the prior distribution of 'unfair' wheelsA Bayesian guess would be 18/37, but for a prior that can be updated, we'd need a distribution where other values are possible. (i.e. the prior distribution ##Pr(P_{red} = 18/37) = 1## can't be updated .)

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Stephen Tashi

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Ok, but you'd have to specify what particular distribution is to be used. There is more than one probability distribution with mean 18/37.No, the P(red)=18/37 is the mean of the prior distribution of 'unfair' wheels

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Stephen Tashi

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In particular the OP asks about the (true) values for probabilities of events in a deterministic system. Are they always either 0 or 1 ?but the OP, as I read it, is questioning the use of probability on deterministic systems

To classify a system as "deterministic" versus "Quantum mechanical" involves assuming a model for it. Any method that acknowedgesAs all non-QM physical systems are deterministic given (typically unknowable) perfect knowledge of its initial conditions then it follows all applications of probability to these systems is an acknowledgement of our ignorance of these conditions.

The general idea that probability is a measure of infomation or ignorance is not specific enough to use in interpreting probability models. To be specific, we have to say what event the ignorance concerns. For example, we can think of one fair coin tossed 100 times and say 1/2 is a measure of our ignorance that the coin will land heads on the 15th toss. Or we can think of an "ensemble" of fair coins that have each been tossed 100 times and each have definitely landed (either heads or tails) on the 15th toss. We can assign 1/2 as measure of our ignorance about which particular type of coin in the ensemble is the one we are dealing with.

The problem of determinism versus probability is but one aspect of the metaphysics of interpreting probability as involving an "actuaL" event that happens from a set of "possible events". In the mathematical treatment of probability as measure theory, there is no definition of "actual" versus "possible" events. There isn't even an axiom that says we can take samples of a random variable.

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On the larger, non-quantum level, if we knew all that God knows and could do all the calculations, we could deterministically predict the outcome. Of coures, we are actually not in that situation and we have incomplete information and knowledge. In that case, probabilities should be thought of as the theory of guessing given incomplete information. A good example is the case where the outcome has already occured, like a prior toss of a coin that we can not see. Clearly the outcome is given and must be considered determined. Yet we have incomplete information and our guess of the outcome must be calculated as probabilistic.

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Stephen Tashi

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In the mathematics section of the forum, we can discuss physical and metaphysical concepts, but (to me) it is important to distinguish mathematical ideas from ideas about physics and metaphysics.In that case, probabilities should be thought of as the theory of guessing given incomplete information.

In mathematical probability theory (meaning the measure theoretic approach) there are no definitions that deal rigorously with the physical concepts of "determinism" and the "actual" occurence of events versus "possible" events that might have occurred. So any questions of how probabilities are to be interpreted when applying mathematical probability theory to a certain real life problem fall outside the scope of mathematics and within the scope of whatever discipline studies the particular problem.

I agree that logic falls within the scope of mathematics. The original post does ask a question that could be considered purely a question of logic - it asks whether probabilities assigned to events in a deterministic process must be either 0 or 1. However, to answer that question, the subject matter to which we apply logic is not mathematical probability theory. The subject matter to which we apply logic involves interpreting concepts like "deterministic" and "actually occurred" in the context of situations in physics or metaphysics.

It is certainly true that people who apply probability theory to situations that are "deterministic" by some definition used in physics, justify their use of probabilities by interpreting the concept of probability as having to do with information or ignorance. The point I wish to emphasize is that this approach is not mandated or prohibited by mathematical probability theory. Mathematical probability theory says "No comment".

People whose background is in applied mathematics often find the rigorous presentation of probability theory repulsive because familiar concepts in applied math like random samples only appear in a ghostly form, such as conditional probability distributions. However, if we consider the many inconclusive debates that arise about the interpretation of probability, then the wisdom of Kolmogorov becomes apparent. He picked out the aspects of applied probability that can be treated rigorously and left the rest to Philosophy.

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