**Akaike information criterion** (**AIC**) measures prediction error and model complexity.

AIC = 2k - 2 log(L), where k is the number of parameters and L is the maximum likelihood.

**Bayesian information criterion** (**BIC**) is closely related to AIC and is based on likelihood and model complexity.

BIC = k log(n) - 2 log(L), where k is the number of parameters, n is the number of parameters, and L is maximum likelihood.

**Censoring** in statistics is when the random variable is truncated at a non-random value. For example, a bathroom scale might only measure up to 140 kg. If a 160-kg individual is weighed using the scale, the observer would only know that the individual’s weight is at least 140 kg.