# Use of transmat matrix in full stochastic unit commitment

```Hello everyone:

I've been working with MOST for some time now; specifically with the unit
commitment problem. I have been able to add some linearised constraints to
it, but now I face the challenge of using the stochastic programming option
in MOST. I've followed the example "most_ex7_suc" for these
cases: Stochastic Unit Commitment - Individual Trajectories and Stochastic
Unit Commitment - Full Transition Probabilities and my questions are the
next ones:```
```
1. I've realised that in the matrix "transmat", only the first column is
full with the three scenarios' probabilities, but for the rest of the
columns we have the identity matrix size 3x3 for each time step.Then the
"scenario_probs" variable is computed/updated with the
operation:  mdi.tstep(t).TransMat * mdi.CostWeights(1, 1:mdi.idx.nj(t-1),
t-1). My question is: Can these scenario probabilities change through time?
I mean, that for every column we could have different scenario
probabilities in "transmat". And if this was the case, what changes would
be needed for the function "ex_transmat" to reflect these changes?

2. For the wind input in the example  "most_ex7_suc"  we have three
different values of wind. I am assuming they belong to the three scenarios
specified but only for that wind input. If we had different wind inputs
from different buses, what would the arrangement of
windprofiles.values(:,:,:) be?

3. How can we see the difference betwen the Individual Trajectories and
Full Transition Probabilities case? I've understood from the manual that
the system "stays" in one path in the case of Individual Trajectories, but
for the Full Transition Probabilities how can we see the transition between
the three scenarios' possible paths?

4. Can we that MOST is a Two-Stage model in Full Stochastic Programming
then?

Right now Im only working with the full system, i.e. no contingencies.

As usual, thank you so much in advance,

--
Carlos Ferrandon
```