Abstract: The present invention is directed to a method for information analysis used to discriminate between biased and unbiased fits. A goodness-of-fit parameter becomes a quantity of interest for analysis of multiple measurements and does so by replacing a likelihood function with a probability of the likelihood function W(L|?). The probability of the likelihood function is derived from a new posterior probability P(?|L) and the goodness-of-fit parameter G, wherein a is a set of fitting parameters, L is the likelihood function and W(L|?) is equal to the product of P(?|L) and G. Furthermore, if substantial prior information is available on ?, then the probability of the fitting parameters P(?) can is used to aid in the determination of W(L|?).

Abstract: The present invention is directed to a method for information analysis used to discriminate between biased and unbiased fits. A goodness-of-fit parameter becomes a quantity of interest for analysis of multiple measurements and does so by replacing a likelihood function with a probability of the likelihood function W(L|?). The probability of the likelihood function is derived from a new posterior probability P(?|L) and the goodness-of-fit parameter G, wherein a is a set of fitting parameters, L is the likelihood function and W(L|?) is equal to the product of P(?|L) and G. Furthermore, if substantial prior information is available on a, then the probability of the fitting parameters P(?) can is used to aid in the determination of W(L|?).