You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
To implement a basic linear model code, we will need a class that extends AEPsychMixin that uses a latent linear model with learnable parameters instead of a GP. At minimum, it needs to support a fit method, and a posterior method so that we can integrate it with acquisition. This issue will require a bit of research to understand where and how to implement everything -- we are happy to support.
The text was updated successfully, but these errors were encountered:
Many classical psychophysics models are simple linear models or linear models with a polynomial basis. We should include them as baselines in AEPsych. For example, psignifit contains many of them (https://psignifit.sourceforge.net/PSYCHOMETRICFUNCTIONS.html). In the language of the psignifit docs, our "core" is a GP model and our sigmoids are the various objectives here https://github.com/facebookresearch/aepsych/blob/main/aepsych/acquisition/objective.py.
To implement a basic linear model code, we will need a class that extends
AEPsychMixin
that uses a latent linear model with learnable parameters instead of a GP. At minimum, it needs to support afit
method, and aposterior
method so that we can integrate it with acquisition. This issue will require a bit of research to understand where and how to implement everything -- we are happy to support.The text was updated successfully, but these errors were encountered: