I am new to using PyMC3 and I am still learning, but very enthusiastic about the initial results.
I have been working for many years with artificial neural networks and always with non-linear multivariate data.
If I have spectral data (eg, Infrared spectra) for a set of food samples, can I build a model to build a model related to this spectra with other (discrete) parameters (eg physicochemical properties) in food analysis?
That is a very broad question, phrased in a very broad way. Could you re-ask it? What does your spectral data look like (dimensions, units, etc)? What kind of models are you thinking of?
Pymc3 has an absolutely excellent examples page on its main site that you can check out for inspiration.
Thanks Gon_F for your answer!
I would like to do a regression model like this:
The Infrared spectra have many variables (usually 1400 values) and always we wrote as a matrix (N x M) with N samples and M variables (absorbances).
In the paper above we use variable selection methods to simplify the input model. I use Principal Components Analysis (PCA) to do this. Is there is something similar?
The output model is % of fat.
Thank you very much for any help!