Research
Continuing on from yesterday. Its important to highlight that ‘method of moments’ and sensitivity analysis, gaussian approximation are all trying to describe py or more specifically the CDF of y. I think this is the point I’m trying to get across.
What is the rationale here for each analysis. I need to describe simple vs a more complicated function. When the surface is planar, nothing past a Gaussian is really needed. This might involve why Taylor series are needed sometimes.
This is what I’m going to do for every I find!
- A basic sensitivity analysis
- Gaussian approximation to incorporate uncertainty of parameter.
- MC Sampling. Due to lack of knowledge about covariance.
- Importance with covariance using (Morgan).
I’ve seen some stuff about regression for sensitivity analysis.
Taylor Series
Generalization of linearization, which makes sense if you think about it.
What is the taylor expansion for a multivariate function?