An application of the stationary phase method for estimating probability densities of function derivatives

08/08/2011
by   Karthik S. Gurumoorthy, et al.
0

We prove a novel result wherein the density function of the gradients---corresponding to density function of the derivatives in one dimension---of a thrice differentiable function S (obtained via a random variable transformation of a uniformly distributed random variable) defined on a closed, bounded interval Ω⊂ R is accurately approximated by the normalized power spectrum of ϕ=exp(iS/τ) as the free parameter τ-->0. The result is shown using the well known stationary phase approximation and standard integration techniques and requires proper ordering of limits. Experimental results provide anecdotal visual evidence corroborating the result.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/13/2012

Gradient density estimation in arbitrary finite dimensions using the method of stationary phase

We prove that the density function of the gradient of a sufficiently smo...
research
04/19/2011

Distance Transform Gradient Density Estimation using the Stationary Phase Approximation

The complex wave representation (CWR) converts unsigned 2D distance tran...
research
08/29/2022

A Random Number Generator for the Kolmogorov Distribution

We discuss an acceptance-rejection algorithm for the random number gener...
research
04/28/2023

Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation

We study numerical integration over bounded regions in ℝ^s, s≥1 with res...
research
06/03/2019

A library to compute the density of the distance between a point and a random variable uniformly distributed in some sets

In [3], algorithms to compute the density of the distance to a random va...
research
10/29/2021

A probabilistic view of latent space graphs and phase transitions

We study random graphs with latent geometric structure, where the probab...
research
02/04/2021

Continuous Random Variable Estimation is not Optimal for the Witsenhausen Counterexample

Optimal design of distributed decision policies can be a difficult task,...

Please sign up or login with your details

Forgot password? Click here to reset