Explain what you mean by meaningless? Your disagreement is very vague.It's meaningless. It doesn't support the OP in anyway, so why bother? Unless the point of the OP is if things were different , they'd be different.
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Explain what you mean by meaningless? Your disagreement is very vague.It's meaningless. It doesn't support the OP in anyway, so why bother? Unless the point of the OP is if things were different , they'd be different.
I gave you a link to understand the whole concept. You asked for support for my statement on tweaking the constants and Bayesian approach to probabilities.
So you are going to ignore what I've posted in support of what I am saying and just denying it out of hand.The scenario presented has a probability of 1. It's as pointless as flipping a two headed coin.
See the last post I provided to Athee.
So you are going to ignore what I've posted in support of what I am saying and just denying it out of hand.So you are going to ignore what I've posted in support of what I am saying and just denying it out of hand.
You wanted to know how Bayesian approach probability is relative to my statement and I provided that information.Why? None of them address my point. You've yet to try and quantify any of the P() terms for the scenario we're discussing and are instead just repeating that we can imagine things being different.
The only person in this thread that is providing anything of scientific merit is myself. No one is really making any effort to show how what I am providing is incorrect scientifically or invalid.So you are going to ignore what I've posted in support of what I am saying and just denying it out of hand.
Or are you saying the point of the thread is "if things were different, they would be different?"
Quoted to preserve plagiarism.You wanted to know how Bayesian approach probability is relative to my statement and I provided that information.
With the Bayesian toolbox in hand, we need not insist on a strict dividing line between responsible extrapolation and reckless speculation. If “successful” multiverse theories — those that correctly predict our fundamental constants — are a dime a dozen, then none will be particularly likely in light of our available evidence. Think of a detective investigating a dead body, a spotless crime scene, and a room full of suspects; without further evidence, the case will remain unsolved. Alternatively, if fundamental theories of space, time, and matter provide a mechanism for generating a variegated ensemble of universes that is simple and well-grounded in known physics, then the multiverse may find a place in science as a reasonable extrapolation of a well-tested theory. Or, just as importantly, it may be discarded, not as an untested speculation but as a scientific failure. Currently, no multiverse theory can claim to be tested to this extent.
Moreover, nothing in the Bayesian approach limits its application to propositions about the physical world. Probabilities are degrees of plausibility, and can in principle be applied wherever human curiosity leads. Even if precise calculations of numerical values are impossible, we can ask the right questions. From the link I provided.
In thinking about these problems, our approach to probability matters. The fine-tuning of the universe for life invites us to imagine that our fortuitous cosmic environment is improbable. A random spin of the cosmic dials, it seems, would almost certainly result in a universe unable to create and sustain the complexity required by life. But if probabilities must be dictated by physical theories and are about physical events, as the frequentist believes, then we cannot say that our constants are improbable. We have no physical theory that stands above the constants, informing us that they are unlikely.
However, within the Bayesian approach, probabilities are not confined within physical theories. We can state that, for example, naturalism — the idea that physical things are all that fundamentally exist — gives us no reason to expect that any particular universe or set of laws or constants is more likely than any other, because there are no true facts about the universe that stand above the ultimate laws of nature. According to naturalism, there is no explanation of why this rather than some other final law, why any law at all, why a mathematical law; no explanation, to borrow the words of Stephen Hawking, of what “breathes fire into the equations and makes a universe for them to describe.” Like the uninformed detective in a large room of suspects, the probability of naturalism is at the mercy of every possible way that concrete reality could be.
No, you only think it is of scientific merit. Saying that we can calculate the probability of flipping a two headed coin is not scientific.The only person in this thread that is providing anything of scientific merit is myself. No one is really making any effort to show how what I am providing is incorrect scientifically or invalid.
See the last post I provided to Athee.
What is true is that, if I’ve observed D, then the probability of D given everything I’ve observed is one. If you feed D into the reasoning robot, and then ask it for the probability of D, it will tell you that it is certain that D is true. Mathematically, p(D|D) = 1.
http://user.physics.unc.edu/~dstark/BayesTheorem.pdfNo, you only think it is of scientific merit. Saying that we can calculate the probability of flipping a two headed coin is not scientific.
I answered it a couple posts back but I will do so again, the answer is no. The human heart has no intrinsic purpose. It has a function within the human body it these are not the same things. I can't help it if most people don't parse language very careful and in truth in most everyday situations conflating the two wouldn't be all that problematic. But the difference is important to our discussion which is why I keep pointing it out.You didn't answer my question, is the purpose of the heart to pump blood? Most people I think would agree that the purpose is to pump blood. The function is how that purpose is fulfilled.
Probability Myth: we’ve observed X, so the probability of X is one
November 18, 2013 by lukebarnes
Continuing with the probability theory, a quick myth-busting. I touched on this last time, but it comes up often enough to deserve its own post. Recall that rationality requires us to calculate the probability of our theory of interest T given everything we know K. We saw that it is almost always useful to split up our knowledge into data D and background B. These are just labels. In practice, the important thing is that I can calculate the probabilities of D with B and T, so that I can calculate the terms in Bayes’ theorem,
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Something to note: in this calculation, we assume that we know that D is true, and yet we are calculating the probability of D. For example, the likelihood. The probability is not necessarily one. So do we know D or don’t we?!![]()
The probabilityis not simply “what do you reckon about D?”. Jaynesconsiders the construction of a reasoning robot. You feed information in one slot and, upon request, out comes the probability of any statement you care to ask it about. These probabilities are objective in the sense that any two correctly constructed robots should give the same answer, as should any perfectly rational agent. Probabilities are subjective in the sense that they are relative to what information is fed in. There are no “raw” probabilities
. So the probability
asks: what probability would the robot assign to D if we fed in only T and B?![]()
Thus, probabilities are conditionals, and in particular the likelihood represents a counterfactual conditional: if all I knew were the background information B and the theory T, what would the probability of D be? These are exactly the questions that every maths textbook sets as exercises: given 10 tosses of a fair coin, what is the probability of exactly 8 heads? We can still ask these questions even after we’ve actually seen 8 heads in 10 coin tosses. It is not the case that the probability of some event is one once we’ve observed that event.
What is true is that, if I’ve observed D, then the probability of D given everything I’ve observed is one. If you feed D into the reasoning robot, and then ask it for the probability of D, it will tell you that it is certain that D is true.
So first off I did not say we have observed x once therfore the probability is 1. I said there is only one instance of x and so the values within that x have a probability of 1, an important difference.
As for the rest of it I think it would really help me if I knew what parts of your hypothesis fit where. If you could define the terms in the equation for me as they stand in your argument. So what do D and B and T represent in your argument?
Ok. Are you going to provide anything that shows he doesn't think the universe appears designed?
http://user.physics.unc.edu/~dstark/BayesTheorem.pdfThe site you're linking to supports exactly what he's saying :
So until you're ready to lay out values for the conditional terms - no doubt derived from your numerous observations of universes forming - then there's nothing we can do with a Bayesian approach. Ball's in your court.
Was that cut and paste supposed to have an cite in it? All of the masses of unrelated info are starting to run together.Excuse me?
So what. In this thread it's being used to assess probability of flipping a two headed coin. I don't need Bayes Theorem to do that. It's 1.http://user.physics.unc.edu/~dstark/BayesTheorem.pdf
You need to understand that this way of assessing probability is not just used in this case but in Physics all the time.
Do you think that the heart has a reason for its function?I answered it a couple posts back but I will do so again, the answer is no. The human heart has no intrinsic purpose. It has a function within the human body it these are not the same things. I can't help it if most people don't parse language very careful and in truth in most everyday situations conflating the two wouldn't be all that problematic. But the difference is important to our discussion which is why I keep pointing it out.
D=Data B=Background information and T=Theory of interest.So first off I did not say we have observed x once therfore the probability is 1. I said there is only one instance of x and so the values within that x have a probability of 1, an important difference.
As for the rest of it I think it would really help me if I knew what parts of your hypothesis fit where. If you could define the terms in the equation for me as they stand in your argument. So what do D and B and T represent in your argument?