-
read:
### stochastic programming
mathematical programming book
http://web.mit.edu/15.053/www/AMP.htm
mutistage stochastic programming
https://orbi.uliege.be/bitstream/2268/80246/1/MSPchap_pre…
-
We have been iteratively coming back to discussion that Stochastic Programming (SP) is one of the most wished features.
There is an [example notebook](https://discord.com/channels/911692131440148490…
Irieo updated
8 months ago
-
Either use shifted multi-stages, or using average multi-stages.
More reference needed.
-
Hello there, there are a few other approaches to this that I have seen and wondered if they are on your radar.
Bellman Conformal Inference (BCI) - optimises prediction intervals for time series …
-
## Description of the issue
This is a rather large project and it is about including concepts from Modelling in the ontology. I am not 100% sure if they even belong but we can discuss that. Here is…
-
We have been playing around with various ways to do scenario sampling based analysis of power systems in oemof. The cleanest option seems to be to leverage the stochastic programming language built on…
FHell updated
2 years ago
-
EXSAMPLE
```
from cvxpy.constraints import nonpos
from cvxpy.constraints.nonpos import NonNeg, NonPos
from cvxstoc import expectation, prob
from cvxstoc import NormalRandomVariable, prob
from cv…
-
I have a multi stage stochastic programming problem. The state variables are continuous variables and the objective functions are linear, but the local problems of some stages are mixed interger linea…
-
- On Lisp
- ANSI Lisp
- SICP (wizard book)
- Paradigms of Artificial Intelligence Programming
- Artificial Intelligence
- Concrete Mathematics
- Mathematics for CS
- Probability, Random Variabl…
-
## Summary
Are there examples of using parmest to simultaneously estimate both "global" parameters across all experiments (stage 1 in the stochastic programming abstraction, i.e., what is done by d…