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128
TL;DR
NOVAH (No Violence At Home) was incubated by Charity Entrepreneurship (now Ambitious Impact) in 2024 to test a promising idea: preventing intimate partner violence through edutainment, in our case a serialised radio drama. Over the past two years we have produced and aired two seasons in Rwanda.
We are currently evaluating our second season through a randomized controlled trial with 2,400 couples in Rwanda in partnership wi...
I think most frequentists would agree that Bayesian inference is more intuitive. Bayesian inference is much more computationally difficult though, and you usually get the same answer anyways. (Bayesian estimators are typically asymptotically equivalent to classical estimators!)
> and you usually get the same answer anyways
I don't agree with this! In reality we don't get asymptotic properties, we get finite sample properties, and these can vary greatly. E.g. MLE often won't even converge for hierarchical models without a fair amount of data. Also, for bespoke models there often isn't a published frequentist estimator available, and attempting to derive one would be a much bigger issue for most people than the computational resources required for MCMC or variational inference.