Markov Drumming Ideas

I need to create a list of statistical attributes that can be analyzed and linked to form a statistical Markov profile for drum styles. I also need to establish how attributes will be stored. Here are some ideas:

Format for statistical attribute: Node-(Trigger:Boolean Operator:Trigger...)=[Correlation],[Strength]

Examples
Snare-(Beat5:AND:CHatLast1)=26.73,2
Snare-(Beat5:AND:!CHatLast1:AND:ChatLast2)=53.9,4

The above attributes specify a few things. First, there is a 26.73% chance that a snare hit will directly follow a closed hi-hat hit on beat 5 (meaning the hi-hat hit falls on beat 4 and the snare hit falls on beat 5). Second, there is a 53.9% chance that a snare hit will follow a closed hi-hat hit by two beats on beat 5 provided that it does not directly follow a closed hi-hat hit (meaning the hi-hat hit falls on beat 3, beat 4 must not be filled by a hi-hat hit, and the snare hit falls on beat 5).

I think that these kinds of logical combinations will allow a thorough analysis of percussive styles.

Absolute Beat
The most basic trigger, this fires on a specific beat number.

Modulus Beat
Very similar to the absolute beat trigger, this trigger fires for each beat number computed modulus a certain divisor. In other words, it may fire every fourth beat, or every other beat, etc.

More on triggers later.

Markov Drumming Ideas

The Problem: Given a Markov analysis module, particularly for percussion, analyzing pieces of different styles (or even a single piece with slight variations in style) and averaging them into a primary statistics file would cause the file to become a "soup" of conflicting styles. This mushy average would turn into a rather nasty output. It's like taking vibrant blue and green, both very nice colors when taken separately, and combining them to get a nasty brown.

Possible Solution: When analyzing pieces, create statistical profiles of each segment (on an individual measure, or maybe a 4-measure basis) and compare the divergence of the statistics. If the divergence measure surpasses a certain threshold (which the user may set), then the segments are treated as separate styles that use separate statistical profiles. If they don't diverge by much, then the statistics can safely be averaged and saved to the main statistical profile for that style. It's like averaging all the shades of red and all the shades of green separately, so as to avoid mixing to get brown. Furthermore, an overarching statistical profile for style transitions could be made so that the drummer knows how often Style A moves to Style B and when, based on the segmented analysis of each piece. In this way, the analysis could conceivable decipher and reproduce an entire sequence of Intro, Verse, Chorus, Verse, etc. without actually understanding what each part means, just knowing that the statistical profiles for each diverge and transition into each other in certain parts of the composition.