Improvements on Tutorial for BEMB with Simulated Data and the obs2prior Option

[TianyuDu]

Tutorial for BEMB with Simulated Data and the obs2prior Option

In this post I review the notebook on obs2prior. Note that I had reviewed the H_zero_mask option which is an extension to this notebook in the post above. Here are some comments

• we could add some context regarding when using observables helps and when it doesn't
• Relatedly, it would be useful to add a summary of what the tutorial will do before we start the simulations. What are we trying to predict, what do we know about the underlying preferences, what variables do we observe, why in this context we expect using obs2prior will help, etc.
• "The observable of a particular user is a one-hot vector with width num_items and one on the position of item this user loves (as mentioned previously)."
  • I thought that this was precisely what we don't observe and try to recover
  • Tianyu's comment: it is a trivial example to show how to implement the model and show that the model understands that this variable is very important
• For internal purposes: often, the user observables relate to demographics (age, gender, income, etc.). Why didn't we choose to simulate this type of situation?
  • eg. linear relationship between age and which item the buyer loves
• "Fitting the Model"
  • it would be useful to talk more about what the package does and what input is necessary. For instance, do we need to set a prior for H and W, does the package sets them by default, does the package tries different ones and selects the best one?

Originally posted by @charlespebereau in gsbDBI/torch-choice#5 (comment)