Hi Johnny
I think you got it all right. A useful abstraction for Markov chains
is the list-wrandom object in the list-abs collection. Also see the
c_markov and c_markovanal objects in the rj-library. They provide both
analysis and generation of markov processes.
Ciao
Frank
On 20.11.2010, at 19:43, Johnny Ferguson <[email protected]> wrote:
Hi,
I just read about Markov chains and how they can be applied to
selecting pitches using probability. One thing I'm wondering is how
we assure that our note selection matches the distribution of
probability.
Let's say our current note was C4.
For the next note:
P(D4) = 0.3
P(E5) = 0.2
P(F4) = 0.5
Would we simply generate a random number R (0..1) and say:
if R < 0.3 then
nextNote -> D4
elsif R < (0.3 + 0.2) then
nextNote -> E5
else then
nextNote -> F4
I only ask, because if our random number function wasn't well-
distributed, we might actually see higher probabilities for certain
notes than we've intended.
Simply put, what is the most effective, efficient, and accurate way to
select the next state based on a list of note-probability pairs
connected to the current state?
I'm all right with math, but probability is not one of the areas I'm
very strong in. If anyone has a simple way of explaining this, I'd
greatly appreciate it.
I can see that markov chains could be applied to far more things than
pitch (modulation, rhythm, structure, etc.), so I want to get at
least a decent grasp on how we implement them reliably before I
start going nuts with the idea.
-Johnny
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