Patent ID: 7324925
Filing Date: 2008-01-29
Classification: G06F

Abstract:
1. A method for discriminating between a biased fit of data and an unbiased fit of data using a probability function, a binned likelihood function, a peak likelihood function, a set of fitting parameters, a goodness-of-fit parameter, and a fitness precision, the method comprising the steps of: collecting data; determining a first set of probabilities for each ordinate data point of said collected data as a function of the fitting parameters and the abscissa value; determining the binned data likelihood by taking the product of said first set of probabilities; determining a first set of values, said first set of values estimating the set of fitting parameters affording a maximum value of the binned likelihood function; determining a second set of probabilities for each ordinate data point as a function of said first set of values and the abscissa value for each data point of said collected data; determining a peak likelihood by taking the product of said second set of probabilities; determining a representation of said peak likelihood in function space; forming a second set of values, said second set of values comprised of said first set of values, the peak likelihood and said representation of said peak likelihood in function space; determining a third set of probabilities for each data point as a function of said first set and said second set of values; determining a goodness-of-fit parameter by integrating said third set of probabilities over said second set of values; and determining a maximum goodness-of-fit parameter, said maximum goodness-of-fit parameter being the maximum value attainable by said goodness-of-fit parameter, wherein the ratio of the goodness-of fit parameter to the maximum goodness-of fit parameter is less than the fitness precision, for the purpose of discriminating between biased and unbiased fits of data.