Guess, I'll move this to a separate thread, as I'm really interested
in discussing this topic. So, what's the right way to calculate a
first guess in a statistic model?
And please don't mention
as its more complex than that.

> - better table analysis with ability to substract successive elements

I'd never seen a single case where a simple delta was the best solution,
actually. Sometimes just a proper 2D context is enough, sometimes its
good to subtract an extrapolation by some previous lines, but never
simple delta was the best.

Btw, I'm currently thinking about a design with dynamic symbol ranking
for unary coding. Well, typical solution is to sort symbols by context
order, then by MtF rank (aka number of different symbols since last
occurence). And it works much better than seemingly "more valid"
solutions like descending probability order. But anyway these are
empiric methods without any foundations.

Well, it might seem unrelated, but its actually the same thing as with
deltas in 2D table encoding. I think its obvious if you consider unary
encoding - delta gives rank0 to the same value as previous etc.

So, my current opinion is that rank0 should be assigned to the symbol
with max codelength in some context (descending codelength order).
Higher (=lesser) rank usually means more complex model and more precise
estimation in unary schemes, so that's why.

Any suggestions?