Estimates the probability density function (PDF) of 1D or 2D signals from the error-reduced statistical histogram. Wire data to the signal input to determine the polymorphic instance to use or manually select the instance.


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WA Probability Density Function Estimation Details

This VI completes the following steps to implement the wavelet-based estimation of the probability density function.

  1. Calculates the histogram of the input signal.
  2. Performs the wavelet denoising on the histogram output.
  3. Rescales the denoised function to return a unit integral.

You often estimate the PDF of a signal or image by computing the histogram for a large number of samples. However, when the realization number of a stochastic process is limited, such as with an image with a fixed size, the PDF estimation from the histogram might include a large variance. In this case, you can use smoothing methods to return a better estimate. The wavelet method can keep the smoothness of the estimated PDF and provide a solution for density functions with breakdown points.

Examples

Refer to the Probability Density Function Estimation VI in the labview\examples\Wavelet Analysis\WAGettingStarted directory for an example of using the WA Probability Density Function Estimation VI.