3 ms·
I'm more pessimistic. My experience has been that inferring often benefits from reparameterization or analytic marginalizing, plus a bag of tricks that many pe
by x0x0 12y ago
I'm more pessimistic. My experience has been that inferring often benefits from reparameterization or analytic marginalizing, plus a bag of tricks that many people don't know.
It's unfortunately still a long way from being a technology that you can pretty blindly use like ols or logistic regression. Particularly if you only care about prediction, not parameter inference, lr with a penalty search is pretty straightforward.
May I ask what / if you use bayesian stats for professionally in your news work?
Also, the link to your github from your site goes to stdbrouw instead of debrouwere
- stdbrouw 12y agoSo do you feel the pitfalls of probabilistic programming / Bayesian data analysis are potentially more severe than those of traditional methods? I don't get to use that much Bayesian analysis in my work, but I've been toying around with a hierarchical model of content popularity recently: are some topics / authors / genres more popular than others? Frankly finding it hard to separate the signal ("this is what people actually like to read") from the noise ("this is what just happened to be trending and will not be reproducible").