Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Approximate probabilistic constraints using linear error propagation theory and Measurements.jl #79

Open
mohamed82008 opened this issue Jun 20, 2021 · 0 comments

Comments

@mohamed82008
Copy link
Member

We should define a probability function over a measurement from Measurements.jl that calculates differentiable probability assuming normality. Then uncertainty can be propagated from the data to the objective or constraints using the linear error propagation theory implemented in Measurements.jl.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant