Abstract: This method for estimating biological or chemical parameters in a sample (E) comprises steps consisting of putting (102) the sample (E) through a processing chain, obtaining a signal representative of said biological or chemical parameters as a function of at least one variable of the processing chain, and estimating (104, 106, 108, 110) said biological or chemical parameters using a signal processing device by Bayesian inference, on the basis of a direct analytical modeling of said signal as a function of said biological or chemical parameters of the biological sample and as a function of technical parameters of the processing chain. At least two of said biological or chemical and technical parameters have a probabilistic dependence relationship between each other and signal processing by Bayesian inference is further accomplished on the basis of modeling by a conditional prior probability distribution of this dependence.

Abstract: A method for estimating molecule concentrations in a sample, the method including: obtaining a signal generated by a sample analyzing apparatus, the signal including at least one spectrum, the spectrum including peaks representative of the molecule concentrations in the sample and being expressed as a function of at least one variable; obtaining an analytical modeling of the signal with a modeling function, wherein the modeling function is based on the molecule concentrations and factors including gain factors of the equipment and description factors of the peaks; selecting uncertain factors in the factors; determining a priori probability distributions for the uncertain factors and the molecule concentrations; establishing, with the modeling function and the signal, posteriori probability distributions for the molecule concentrations and the uncertain factors; and estimating, with a processing module, the molecule concentrations and the uncertain factors from a parameter inferred from the posteriori probabil

Abstract: This method for estimating molecule concentrations in a measurement line that can include a liquid phase chromatographic column or a mass spectrometry is based on statistical calculations to transform, in an analytical model of the received signal, the factors (measured results or measurement parameters) into probability distributions from a priori estimations. The final values of the results are inferred from a parameter, such as the average, of these probability distributions. The method is excellent in properly assessing measurement peaks in signals with noise or superimposed with other peaks, which is not carried out properly by conventionally peak analysis or spectral analysis methods.