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Histograms for Probability Density Estimation: A Primer
c density estimation, a.k.a fitting probability distribution to observed data by tweaking the parameters of the distribution’s functional form, is all the rage now with generative modeling and LLMs. I think nonparametric methods deserve some love too and I hope to give a very small primer on these methods in a series of posts.
Note how the new observations/samples are concentrated around the regions where the old observations are, which means that our estimate is reasonably accurate and a good representation of the underlying density. Unlike other nonparametric methods, histograms do not require us to store the observations, which is great if the data set is large and we have memory constraints. In the next post, I will describe a very popular nonparametric method, Kernel Density Estimation, that also follows strategy 1 and is much more scalable to higher dimensions than histograms.
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