Discussion items from 1/28/04
Here are some interesting discussion items that came up on 1/28
1. Just as with Markovian nature, statitionarity of a process too is a
function of the specific model used. You can make a non-stationary
process stationary by adding additional variables whose influence was
ignored earlier
The order of a markov process is the oldest time-slice state
variable that affects the current state. If our state is made up
for 10 random variables X1..X10, then we can have a second order
markov process where x1..x5 at time t are dependent on x1..x5 at time
t-1, while the x6..x10 at time t are dependent on x6..x10 at time
t-2.
2. Dynamic bayes nets are to bayes nets as Situation Calculus is to
FOPC. Specifically:
--> A k-horizon unfolding of a dynamic bayes net just corresponds
to a normal (static) bayes net.
--> The CPTs of a normal bayes net are partitioned into two
separate classes in the case of DBN--The "transition model"
and the "sensor model".
--> If you have evidence on e1...en, and you want to find the
distribution of Xk (k