group-inference's People
group-inference's Issues
add topic model
make the topic model
Next steps 200622
-
1. keep working on posterior predictive check
- * some information criterion, likelihood measure
- * graphing data over predicted distribution
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2. interpretability
- * transitions/combination that most distinguish the clusters
- * some divergence mesuares, KS test maybe
Next steps
- try to get smoother K-metric curve by โ nsteps in sparse-learning and subsetting input/prediction set
- inference performance as a function of N observed transitions, make computationally feasible by subsetting
design optimal experiment
- debug multi_obs model (expand out
assignment
) - search for indices (i.e. transitions) that are maximally informative about group
directly comparing topic model & group model
- explore the relation between number of groups/topics and modeling results (f(K))
- compare the posterior predictions between group and topic models
- interactions between the two items above: do the groups and the topics correspond between the two models? do they diverge when K gets larger? rationale being the two models can be seen as soft/hard versions of the same thing, so maybe they will just pick up similar structure anways.
- robert's hypothesis is that they will diverge
- add stickbreaking to topic model
Additional thoughts
- is there more variability among people's target-specific predictions than their generic predictions
- hierarchical models that allow for specific partner level variations? (socio-linguisticky)
- scoping - specific to the concept of emotion predicition, or something about generc-specific dynamics in social cognition?
Next steps
- try on other data
- e.g. ratings for close others vs. self-ratings
- normalizing vs. not normalizing the rows of the transitions
- diagonal vs. no diagonal
- double-check model output using MCMC against the variational inference (and running with multiple random seeds)
- Look at predictions of mixture model to see how well it's fitting, and examine residuals
- Making more clustering (UMAP/tsne) visualizations
more to-dos
- refactoring the code, separate out the models into a script, separate out data preprocessing, and maybe save the preprocessed data as objects
- finish up topic model for normalized data
- the two should give the same thing when K = 1
- when K = 2 how much do the groups and topics correspond
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