In statistics, the<unk> , named after Carl Friedrich<unk>  and Andrey<unk> , states that in a linear model in which the errors have expectation zero and are uncorrelated and have equal variances, a best linear unbiased estimator (BLUE) of the coefficients is given by the least-squares estimator. The errors are not assumed to be normally distributed, nor are they assumed to be independent (but only uncorrelated — a weaker condition), nor are they assumed to be identically distributed (but only having zero mean and equal variances).
Gauss–Markov theorem
Franck-Condon principle
Hund's rule of maximum multiplicity
Bayes' theorem
Ancillary statistic
Admissible decision rule
Degrees of freedom 
Bayesian inference