Editing Psionics (section)

== Information Theory Equations ==
=== Shannon Entropy ===
<math>H(X) = -\sum_{x \in X} p(x) \log p(x)</math>
* Provides a measure of uncertainty, which could be applied to quantify the information content of psychic experiences or communications.
* Offers mathematical tools for analyzing the complexity of psychic phenomena, including telepathy and precognition.
* Allows for the quantification of the amount of information potentially transmitted through psi-mediated channels.

=== Mutual Information ===
<math>I(X;Y) = \sum_{x \in X}\sum_{y \in Y} p(x,y) \log \frac{p(x,y)}{p(x)p(y)}</math>
* Measures the amount of information obtained about one random variable through another, relevant for studying psi-mediated information transfer.
* Provides a framework for quantifying the degree of correlation between psychic experiences in different individuals.
* Offers mathematical tools for analyzing experimental data related to telepathy, clairvoyance, and other psychic phenomena.

=== Conditional Entropy ===
<math>H(X|Y) = -\sum_{x \in X}\sum_{y \in Y} p(x,y) \log \frac{p(x|y)}{p(x)}</math>
* Measures the uncertainty remaining about a random variable after another random variable is known, applicable to studying the influence of contextual factors on psychic abilities.
* Offers insights into the conditional probabilities involved in psi-mediated interactions, such as the influence of emotional states on telepathic communication.
* Provides mathematical formalism for analyzing the role of feedback mechanisms in psi phenomena.

=== Kullback-Leibler Divergence ===
<math>D_{KL}(P||Q) = \sum_{x} P(x) \log \frac{P(x)}{Q(x)}</math>
* Measures the difference between two probability distributions, useful for comparing observed and expected outcomes in psi experiments.
* Offers a way to quantify the discrepancy between actual and predicted psychic phenomena, aiding in hypothesis testing and model refinement.
* Provides mathematical tools for assessing the fidelity of information transmission in psi-mediated communication.

=== Fisher Information ===
<math>I(\theta) = E\left[\left(\frac{\partial}{\partial\theta} \log f(X;\theta)\right)^2\right]</math>
* Measures the amount of information that an observable random variable carries about an unknown parameter, relevant for studying the underlying mechanisms of psychic phenomena.
* Offers insights into the sensitivity of psychic abilities to various factors, such as the emotional state of the practitioner or the target.
* Provides mathematical tools for optimizing experimental designs and protocols in psi research.