Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve

Abstract

Across the scientific realm, we find ourselves subtracting or dividing stochastic signals. For instance, consider a stochastic realization, $x$, generated from the addition or multiplication of two stochastic signals $a$ and $b$, namely $x=a+b$ or $x=ab$. For the $x=a+b$ example, $a$ can be fluorescence background and $b$ the signal of interest whose statistics are to be learned from the measured $x$. Similarly, when writing $x=ab$, $a$ can be thought of as the illumination intensity and $b$ the density of fluorescent molecules of interest. Yet dividing or subtracting stochastic signals amplifies noise, and we ask instead whether, using the statistics of $a$ and the measurement of $x$ as input, we can recover the statistics of $b$. Here, we show how normalizing flows can generate an approximation of the probability distribution over $b$, thereby avoiding subtraction or division altogether. This method is implemented in our software package, NFdeconvolve, available on GitHub with a tutorial linked in the main text.

References in corpus (6)

• Normalizing Flows: An Introduction and Review of Current Methods
• Extreme deconvolution: Inferring complete distribution functions from noisy, heterogeneous and incomplete observations
• Maximum Entropy and Bayesian Data Analysis: Entropic Priors
• normflows: A PyTorch Package for Normalizing Flows
• Entropic dynamics on Gibbs statistical manifolds
• Note on exponential families of distributions