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# Applying a discrete-time, non-stationary nucleotide model

We fit a discrete-time Markov nucleotide model. This corresponds to a Barry and Hartigan 1987 model.

Note

DLC stands for diagonal largest in column and the value is a check on the identifiability of the model. `unique_Q` is another identifiability check, but it not applicable to a discrete-time model and so remains as `None`.

Looking at the likelihood function, we see these maximum likelihood estimated values

## Get a tree with branch lengths as paralinear

This is the only possible length metric for a discrete-time process.

## Getting parameter estimates

For a discrete-time model, aside from the root motif probabilities, everything is edge specific. But note that the `tabular_result` has different keys from the continuous-time case, as demonstrated below.