Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
A Markov chain could express probabilities like completing "the original" to -> "poster" (0.1), -> "McCoy" (0.2), -> "and best" (0.7) which I don't think this does. But I am tired and maybe also misunderstanding.
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I don't think so. It's basically just traversing nested arrays and picking a random member on each level.
Yep. Sounds like a Markov chain to me.
Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
That's a markov chain of the 1st order.
This is higher order Markov chain
I think Markov chains have transition probabilities, which this model is lacking. But it's the same idea, just with uniform transition probabilitis.
It seems like it has transition probabilities.
Depending on the type of the prior word, it randomly selects the next word from a list of compatible word types.
Am I misunderstanding?
It only has probability 0 or 1/n, where n is the number of compatible next words.
There are no numbers in https://github.com/adamjgrant/Tiny-Predictive-Text/blob/main...
A Markov chain could express probabilities like completing "the original" to -> "poster" (0.1), -> "McCoy" (0.2), -> "and best" (0.7) which I don't think this does. But I am tired and maybe also misunderstanding.
Is a Markov chain where every state has an equal probability not a Markov chain?
Kind of splitting hairs here I guess, but I genuinely don’t know.