A Data Channel Model for Precognitive Signal Reception Using Multiple Trials by Laurentiu Bucur, Ph.
For any N- trials perception process with two equally probable binary outcomes, the following implicit assumption holds:
Each trial j contains two confidence rankings CR1[j] and CR2[j] for each possible outcome. Each sample of the Delta signal CR1[j] – CR2[j] contains a small level of signal observed (in ARV – precognition signal) relative to a superimposing potentially high level of additive noise from multiple sources (environment, judging, etc.). The common intuition and evidence states that nesting multiple trials produces better results in terms of detecting the hidden signal from the noise with a certain confidence level. Below is the framework that shows a mathematical model of such an assumption and gives a simple set of statistical formulas to quantify the confidence level in the detection of the unknown signal after an N-trial perception process, from the raw CR data. The work follows the prevalent assumption in our social groups and in the literature as well as in philosophy that somehow we are able to download information from a noisy data channel, or “tune in” to a noisy universal radio station and pick up subtle signals related to future or past events.
The following method will provide a confidence level-based formula for precognitive signal detection and the associated direction: positive for Outcome 1, negative for outcome 2.