[HN Gopher] Bayesian histograms for rare event classification
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Bayesian histograms for rare event classification
Author : dionhaefner
Score : 51 points
Date : 2021-10-15 20:02 UTC (3 days ago)
(HTM) web link (dionhaefner.github.io)
(TXT) w3m dump (dionhaefner.github.io)
| contravariant wrote:
| Calling this Bayesian seems a bit like wishful thinking at the
| moment, you could just as easily have called it frequentist as
| the main mechanism is merging adjacent bins based on a p-value.
|
| A truly Bayesian approach would require specifying a likelihood
| function for the data based on the choice of bins and turning
| this into a posterior distribution on the choice (and number) of
| bins.
|
| Calculating the maximum likelihood estimate for the simplest such
| likelihood function (samples within a bin are uniform + the
| number of bins is geometrically distributed) can be done with a
| vaguely similar algorithm, but simply merging adjacent bins
| greedily is almost certainly biasing the result right now.
| clircle wrote:
| A very odd method where logistic regression would have sufficed.
| I would have done something like library(mgcv)
| gam(y ~ s(x), data, family = binomial)
|
| to do a wiggly logistic regression, and avoid the binning issue
| entirely.
|
| You can do it Bayesianly if you like, but I don't see why we
| should discretize the data into buckets.
|
| And what am I supposed to take away from the normalized
| histogram?
| CrazyStat wrote:
| Interesting. You've invented something like the Optional Polya
| Tree [1], except from a bottom up instead of top down
| construction.
|
| [1]
| https://www.researchgate.net/publication/47278398_Optional_P...
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(page generated 2021-10-18 23:01 UTC)