People with the benefit of hindsight failing to realize how reasonable vitalism sounded at the time is precisely why they go ahead and propose similar explanations for consciousness, which seems far more mysterious to them than biology, hence legitimately in need of a mysterious explanation. Vitalists were merely stupid, to make such a big deal out of such an ordinary-seeming phenomenon as biology—consciousness is different.
This is precisely one of the ways in which I went astray when I was still a diligent practitioner of mere Traditional Rationality, rather than Bayescraft. The reason to consider how reasonable mistakes seemed without benefit of hindsight, is not to excuse them, because this is to fail to learn from them. The reason to consider how reasonable it seemed is to realize that not everything that sounds reasonable is a good idea; you’ve got to be strict about things like yielding increases in predictive power.
Do you have something on the difference between Traditional Rationality and Bayescraft?
I am finally taking Prob. & Stats next semester (and have not yet looked at the book to see how Bayes figures into it yet. I am going to be pissed if it doesn’t enter into the class at this point), so I figure that I will get my formal introduction to Bayes at that point. However, I do know the Basic P(A|B) = [ P(B|A) P(A) ] / P(B).
And, I can regurgitate Wikipedia’s entries on Bayes, yet I don’t seem to have any real context into which I can place the difference between Bayes and traditional Probability distributions… Can you help, please?
I am currently taking Stats(AP class in the USA, IB level elsewhere), and hope that I can help. A traditional probability test will take four frequencies(Male smokers, female smokers, male nonsmokers, and female nonsmokers) and tell you if there is a correlation with an X^2 test. Bayescraft lets you use gender as a way to predict the likelihood of smoking, or use smoking to predict gender.
The fundamental difference, as far as I can tell, is that Statistics takes results about samples and applies them to populations. Bayescraft takes results about priors and applies them to the future. The two use similar methodology to address fundamentally different questions.
People with the benefit of hindsight failing to realize how reasonable vitalism sounded at the time is precisely why they go ahead and propose similar explanations for consciousness, which seems far more mysterious to them than biology, hence legitimately in need of a mysterious explanation. Vitalists were merely stupid, to make such a big deal out of such an ordinary-seeming phenomenon as biology—consciousness is different.
This is precisely one of the ways in which I went astray when I was still a diligent practitioner of mere Traditional Rationality, rather than Bayescraft. The reason to consider how reasonable mistakes seemed without benefit of hindsight, is not to excuse them, because this is to fail to learn from them. The reason to consider how reasonable it seemed is to realize that not everything that sounds reasonable is a good idea; you’ve got to be strict about things like yielding increases in predictive power.
Do you have something on the difference between Traditional Rationality and Bayescraft?
I am finally taking Prob. & Stats next semester (and have not yet looked at the book to see how Bayes figures into it yet. I am going to be pissed if it doesn’t enter into the class at this point), so I figure that I will get my formal introduction to Bayes at that point. However, I do know the Basic P(A|B) = [ P(B|A) P(A) ] / P(B).
And, I can regurgitate Wikipedia’s entries on Bayes, yet I don’t seem to have any real context into which I can place the difference between Bayes and traditional Probability distributions… Can you help, please?
Never let the official curriculum slow you down! But still approach things systematically, find yourself a textbook.
I am currently taking Stats(AP class in the USA, IB level elsewhere), and hope that I can help.
A traditional probability test will take four frequencies(Male smokers, female smokers, male nonsmokers, and female nonsmokers) and tell you if there is a correlation with an X^2 test.
Bayescraft lets you use gender as a way to predict the likelihood of smoking, or use smoking to predict gender. The fundamental difference, as far as I can tell, is that Statistics takes results about samples and applies them to populations. Bayescraft takes results about priors and applies them to the future. The two use similar methodology to address fundamentally different questions.