Normalizing Flows

Normalizing Flows

Normalizing flows are a powerful method for modeling complex distributions in statistics and machine learning. This method involves transforming a probability density through a series of invertible mappings, allowing for the generation of arbitrarily complex distributions. How Normalizing Flows Work The basic rule for the transformation of densities in normalizing flows involves using an invertible, smooth mapping to transform a random variable with a given distribution. The resulting random

QuantTree histograms

QuantTree histograms

Overview of QuantTree QuantTree is a nonparametric statistical testing technique that constructs a histogram from a set of data points. It recursively splits a multi-dimensional space, such as $\mathbb{R}^d$, based on a stochastic process that determines the proportion of data points in each bin. This method was developed to examine whether a batch of data is drawn from an unknown $d$-variate probability distribution, $\phi_0$, or not. It uses test statistics, like the Pearson statistic, which

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