# Frequency weighting to reduce the computational burden
ADMB Files
Code: binomial_w2.tpl Data: binomial_w2.dat Initial values: binomial_w2.pin Expected Results: binomial_w2-expected-results.par
| ### Model description For categorical data with a small number of possible outcomes it is often possible use frequency weighting to reduce the computational burden. The basic idea is that you only have to evaluate the likelihood once for each outcome value, not once for each observation. Hence there is no limit to the number of observations you can handle, as long as the number outcome categories stays fixed. Further details are given in following example