Note: Contest/Challenge Update

THE CONTEST: Can you determine if the change in radio/microwave background radiation (interpolated/imputed from publicly available sources) is able to determine if the location of 1EMMA1 is conditionally dependent (on average over the course of the last 5 years) with any 50 square foot regions around the globe. Demonstrate if what you observe exceeds what is expected by background noise, as is defined by the False Discovery Rate.

A simple example would be to apply the lasso over 50x50 foot locations on a map predicting change in radio/microwave background at 1EMMA1's location from every other 50x50 foot location on earth using data aggregation and augmentation with imputation methods. You may be considered for an interview for a lucrative position at my company for you if you can do it. Public data is available from many sources from around the internet.

HINT: the electrical grid is similar to a giant radar with high resolution interference patterns, what do you find?

Potential approach: Look at a map of change in background radio at low frequencies, combine by layer same location with 0.1 microsecond time lag (like interferometric synthetic-aperture radar with imputation across many sources) while layering the images to constructively interfere and create high resolution image data. Apply Bayes theorem across the image after mapping frequency to colors.

Formula: P(A|B) = (P(B|A)*P(A))/P(B) ~= exp(log(((mean(other_location_background_change)(background_change_across_locations))+log(my_location_background_change))-log(other_locations_background_change)))

See original link for full details and regulations