Skip to content

Gives analytic formulas to calculate autocovariance matrix and autocorrelation matrix for averaged Wiener process with equal-distance time points. Is supplemented with Python numpy code to verify those formulas with a Monte Carlo simulation.

License

Notifications You must be signed in to change notification settings

ipgmvq/averaged_wiener_process_autocorrelation_autocovariance

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 
 
 

Repository files navigation

The averaged Wiener process autocovariance and autocorrelation matrices and code to verify those with a Monte Carlo simulation

This repository gives analytic formulas to calculate the autocovariance matrix and autocorrelation matrix for an averaged Wiener process with equal-distance time points. Those matrices are supplemented with Python numpy code to verify those formulas with a Monte Carlo simulation.

The file is in the format for Jupyter Notebook / JupyterLab.

The html rendition is best viewed at the URL https://ipgmvq.github.io/averaged_wiener_process_autocorrelation_autocovariance/wiener.html .

About

Gives analytic formulas to calculate autocovariance matrix and autocorrelation matrix for averaged Wiener process with equal-distance time points. Is supplemented with Python numpy code to verify those formulas with a Monte Carlo simulation.

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published