Recent Projects

# Convention Prediction with a Bayesian Hierarchical Multinomial Model

Here, I use a bayesian hierarchical multinomial model to predict the first ballot results at the 2018 DFL (Democratic) State Convention, with data aggregated to the Party Unit level (ex: State Senate district) to guarantee anonymity. While using aggregated data obviously isn’t ideal, this sort of strategy shows a lot of promise, especially if individual level predictors could be harnessed as another level of the hierarchical model. As it stands, this is mostly a proof of concept for bayesian hierarchical models in this context. To use something like this in practice, one could use prior predictive simulation to game out the convention under various assumptions, or condition on the first ballot data and use it to analyze trends in support and predict subsequent ballots as your floor team collects further data.

Continue reading# Type S/M errors in R with retrodesign()

This is a online version of the vignette for my r package **retrodesign**. It can be installed with:

# Why the normal distribution?

When I was first studying probability theory as an undergrad, I had a bit of a conceptual hang-up with the Central Limit Theorem. Simulating it in R gave a nice visual of how each additional random variable smoothed out some of the original distribution’s individuality, and asymptotically we were left with a more generic shape. The proofs were relatively straightforward. One part, however, didn’t really make sense to me. My problem was this: **Of all the many possible distributions, why is the normal distribution in particular that our i.i.d random variables converge to in distribution?**

# Predicting race part 1- Bayes' rule method and extensions

Race is a defining part of political identity in the United States, and so it should be no surprise that accurately modeling race can be beneficial for many political campaign activities. For instance, many organizations work to improve turnout in specific communities of color, or want to target persuasion on a given issue to certain racial group. Alternatively, race and ethnicity might be desired as an input to a larger voting or support likelihood model, given that race is generally predictive of both voting likelihood and candidate support.

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