wollery
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- Joined
- Feb 27, 2003
- Messages
- 11,308
It's very simple.
Your basic hypothesis is poorly defined in scientific terms. because of this the complement is poorly defined.
However, it's worse than that. In Bayesian terms the complement should have specific measurable qualities, in exactly the same way that the hypothesis should have specific measurable qualities. In your version of the Bayesian equation neither has measurable qualities, let alone specific ones, and there's no measurable data that can be used by which to measure the output.
Your parameters are utterly model dependent, and you are merely making wild guesses at their values with no possible way of knowing what they might actually be.
In summary, your approach is unscientific, your hypothesis is poorly defined, the complement to your hypothesis is almost completely undefined, your assumptions are baseless and your application of Bayes theorem is worthless.
Does that help?
Your basic hypothesis is poorly defined in scientific terms. because of this the complement is poorly defined.
However, it's worse than that. In Bayesian terms the complement should have specific measurable qualities, in exactly the same way that the hypothesis should have specific measurable qualities. In your version of the Bayesian equation neither has measurable qualities, let alone specific ones, and there's no measurable data that can be used by which to measure the output.
Your parameters are utterly model dependent, and you are merely making wild guesses at their values with no possible way of knowing what they might actually be.
In summary, your approach is unscientific, your hypothesis is poorly defined, the complement to your hypothesis is almost completely undefined, your assumptions are baseless and your application of Bayes theorem is worthless.
Does that help?