Hi Physics Guy,
I don't want to pile on here but could you explain why my examples are different than yours. Here they are:
Physics Guy wrote:
On the hypothesis that a creator God exists, the universe should surely have had a beginning.
On the hypothesis that the Nosoi exist, humans and other animals should surely suffer from cancers.
On the hypothesis that Poseidon exists, there should exist storms at sea.
On the hypothesis that Pele exists, we should find volcanoes on our planet.
Physics Guy wrote:
By Bayes's Theorem, the actual observation of a finitely old universe with life favors the God hypothesis over the no-God hypothesis, because the God hypothesis requires the observed data, while the no-God hypothesis is merely compatible with them.
By Bayes's Theorem, the actual observation of humans and animals with cancers favors the Nosoi hypothesis over the no-Nosoi hypothesis, because the Nosoi hypothesis requires the observed data, while the no-Nosoi hypothesis is merely compatible with them.
By Bayes's Theorem, the actual observation of storms at sea favors the Poseidon hypothesis over the no-Poseidon hypothesis, because the Poseidon hypothesis requires the observed data, while the no-Poseidon hypothesis is merely compatible with them.
By Bayes's Theorem, the actual observation of volcanoes on our planet favors the Pele hypothesis over the no-Pele hypothesis, because the Pele hypothesis requires the observed data, while the no-Pele hypothesis is merely compatible with them.
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Bayes’s theorem, touted as a powerful method for generating knowledge, can also be used to promote superstition and pseudoscience
Here’s a fuller version: The probability that a belief is true given new evidence equals the probability that the belief is true regardless of that evidence times the probability that the evidence is true given that the belief is true divided by the probability that the evidence is true regardless of whether the belief is true. Got that?
The basic mathematical formula takes this form: P(B|E) = P(B) X P(E|B) / P(E), with P standing for probability, B for belief and E for evidence. P(B) is the probability that B is true, and P(E) is the probability that E is true. P(B|E) means the probability of B if E is true, and P(E|B) is the probability of E if B is true.
https://blogs.scientificamerican.com/cr ... -big-deal/So whatever the probabolity that there was a god before we knew about the big bang and the fact of common ancestry is P(B).
P(E|B) we can take to be certainty, the most favorable for your position here.
P(E) is also certainty. We know the universe had a beginning and we know that the laws are such that evolution occurred.
So P(B|E) = P(B) and nothing has changed according to this as a result of our knowledge that the universe had a beginning and the laws allow for life to evolve.
Of course for the LDS position, P(E|B) is less than certainty, and the probability that there is a god diminishes with a universe that had a beginning and laws that favor evolution.
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Here is my more general statement of that principle: The plausibility of your belief depends on the degree to which your belief--and only your belief--explains the evidence for it. The more alternative explanations there are for the evidence, the less plausible your belief is. That, to me, is the essence of Bayes’ theorem.
So actually since there are other possible explanations for the universe to have had a beginning other than god and same with the laws that allow for evolution the probability for god drops with this new evidence.
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“Alternative explanations” can encompass many things. Your evidence might be erroneous, skewed by a malfunctioning instrument, faulty analysis, confirmation bias, even fraud. Your evidence might be sound but explicable by many beliefs, or hypotheses, other than yours.
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The potential for Bayes abuse begins with P(B), your initial estimate of the probability of your belief, often called the “prior.” ...
If your evidence is flimsy, Bayes’ theorem won’t be of much use. Garbage in, garbage out. ...
In many cases, estimating the prior is just guesswork, allowing subjective factors to creep into your calculations. You might be guessing the probability of something that--unlike cancer—does not even exist, such as strings, multiverses, inflation or God. You might then cite dubious evidence to support your dubious belief. In this way, Bayes’ theorem can promote pseudoscience and superstition as well as reason.
Embedded in Bayes’ theorem is a moral message: If you aren’t scrupulous in seeking alternative explanations for your evidence, the evidence will just confirm what you already believe.
And this is troubling:
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And as I mentioned above, some string and multiverse enthusiasts are embracing Bayesian analysis. Why? Because the enthusiasts are tired of hearing that string and multiverse theories are unfalsifiable and hence unscientific, and Bayes’ theorem allows them to present the theories in a more favorable light. In this case, Bayes’ theorem, far from counteracting confirmation bias, enables it.
Here is another explanation of Baye's:
https://betterexplained.com/articles/an ... s-theorem/I don't see how you could apply it to arrive at the conclusion you do. I'm open for corrections. It seems this would be big news if your viewpoint on this were correct, no?