Well, I'd like to discuss many things... eg. what about entropy-coded contexts? That is, how contexts can have internal correlations and uneven distributions, and how to avoid that.

SSE is magical stuff, so I can discuss it forever ;)

Eg. it works due to a index duality, similar to the wave-particle one in physics. That is, the index (probability) you feed into SSE is a relative frequency of event occurences, and a context (history hash) at the same time. And SSE uses the first interpretation for merging the statistics of similar contexts and general stabilization (initialization as a direct mapping guarantees that secondary estimation is never significantly worse than index probability), and still easily adapts to the cases where context interpretation is preferred.

Of course, that's concerning my own SSE, which I'm trying and failing to improve during last 5 years, so believe me, I know what I'm talking about. There're ugly things like SSE coefficients getting out of [0..1] interval, and additional coefficients for dynamic update speed, and complicated update formula in integer version, but it gets worse if I'm trying to change anything - and my conclusion after all these years of analysis is quoted above.

And I think that similarity is extreme, considering statistical nature of quantum mechanics.

As to "how" - its simple. These two interpretations do simultaneously apply to the same SSE index probability, right? Counter is supposed to contain a relative frequency by definition and its value has a high correlation with last bits in context history, right?

Then, the data in different context instances is supposed to have different properties (or there'd be no sense in using this context at all), So there're context instances without significant correlations in the history, and simple linear mapping (even static) is good for them as a compensation for prediction imprecision. And there're some which do benefit from further context modelling, where SSE coefficients are used just like another counter array.

Now, the problem is that this subdivision is not anything stable - the same context can contain completely random sequences and runs of the same value. And thats why I still cannot replace SSE with something else - damned thing works good enough in both extreme cases and allows for smooth transitions. But still it just has to be replaced with interval-coded contexts mix or something, as its not perfect in any specific case - just that nothing available is that adaptable.

1. Its naive to think that its possible to design a simple-and-clean universal compression model with state-of-art performance. Good model just has to be complicated these days.

2. We're talking about different kinds of SSE here. Eg. paq8 mappings are closer to original SSE in ppmonstr despite the interpolation. And I was talking about my SSE, like the one used in ash, which is significantly more efficient.