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 adja

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.