justindomke

3 ms·

1. 1.▲
   Show HN: NumPy+Jax Except with Named Axes(github.com)

   15 points by justindomke 2y ago|1 comments
2. 2.▲
   by justindomke 3y ago

   I've long been suspicious of this study from Ariely from 2003. It suggests that if you "prime" students by having them write down the last 2 digits of their student ID, then students with numbers like "93" will then
3. 3.▲
   Pangolin is a probabilistic programming interface focused on fun(github.com)

   1 points by justindomke 4y ago|0 comments
4. 4.▲
   by justindomke 5y ago

   I agree that often in practice a confidence interval will end up being similar to a Bayesian credible interval. However, having a flat prior is not enough to guarantee this: the post gives an example with a uniform prior and a valid 70% con
5. 5.▲
   Confidence Games(justindomke.wordpress.com)

   34 points by justindomke 5y ago|6 comments
6. 6.▲
   by justindomke 5y ago

   In higher dimensions, you could do a series of 1d regressions, something like this: 1. Regress x1 against y. Call your curve f1(x1). 2. Regress x2 against (y - f1(x1)). Call your curve f2(x2). 3. Regress x3 against (y - f1(x1) - f2(x2)). Ca
7. 7.▲
   The Human Regression Ensemble(justindomke.wordpress.com)

   53 points by justindomke 5y ago|5 comments
8. 8.▲
   by justindomke 6y ago

   A helpful way to think about burn-in is that you probably don't care about bias as such but rather error which is a combination of bias and variance. Burn-in reduces bias at the cost of variance (since you have fewer points in your f