This is equivalent to a maximum likelihood approach. If the individual activities of a group of motif sequences are equal, the maximum likelihood weight matrix elements are logarithms of observed frequencies for each nucleotide at a position divided by the probability that the nucleotide occurs by chance. The weight element for a T at this position, . Weight matrices have also been obtained using neural network methods. A computer program is trained on examples of good and bad promoters.
Weights are flexible and optimized to discriminate between the training set examples.
Such methods do not seem to give appreciably better results than a maximum likelihood approach. However, they can be more easily adapted to include additional information about what makes a good motif. Consider the case of promoter recognition. Many promoters require additional proteins to initiate transcription. These recognize their own DNA motifs, usually located near the sigma factor element.