[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)