Catogorising strings into random versus non-random

[duncan smith]

On 21/12/15 16:49, Ian Kelly wrote:
> On Mon, Dec 21, 2015 at 9:40 AM, duncan smith <duncan at invalid.invalid> wrote:
>> Finite state machine / transition matrix. Learn from some English text
>> source. Then process your strings by lower casing, replacing underscores
>> with spaces, removing trailing numeric characters etc. Base your score
>> on something like the mean transition probability. I'd expect to see two
>> pretty well separated groups of scores.
> 
> Sounds like a case for a Hidden Markov Model.
> 

Perhaps. That would allow the encoding of marginal probabilities and
distinct transition matrices for each class - if we could learn those
extra parameters.

Duncan