How to create Bayesian models for continuous data?

How can I extract information from continuous data and use it as predictors in a bayesian model?

Thanks for any help!

That’s quite a wide-open question, do you mean time-series data?

There’s a handful of example notebooks here that might help: examples_notebooks — PyMC3 3.10.0 documentation see the sections for Time Series and (if you’re feeling brave) Gaussian Processes

Hello jonsedar!
I work with spectroscopy, so my continuous data are matrices that contain energy variation (intensities) as a function of wavelength.

They are not time series but the examples that you mentioned can be applied to my data type?

Thanks for your answer!

Ah okay, interesting - a million years ago I spent a few months working with an SEM, while my colleague across the hall used a huge XPS machine. Both analyzing silicon on glass. Anyhow.

Are you hoping to do some pattern recognition on the spectra? Quantify uncertainty somehow?

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Yes, I would like to make a calibration model and predict the concentration of organic compounds.

If I were to make this model with an artificial neural network, I could apply Principal Component Analysis to this matrix of spectra and use the eigenvalues obtained as input to the model and the concentration of the organic compound as the output.
I don’t know if the eigenvalues have a Normal distribution and if they could be used as predictors of a Bayesian model. They can?

Thank you!

So to paraphrase (if I may), you want to decompose a signal into a set of features that you can then dump into a (linear | non-linear) model and regress onto a continuous target feature?

Sounds like regular old generalised linear regression and is absolutely possible. You might find some food for thought in the examples Notebooks, and some general statistics reading including: