Math

[fluffy]

I just spent 12 hours scrolling through Wikipedia math articles. Not sure how much I learned but it was not directed in my searching. Mostly I tried to learn quantum physics. I found out that phi is the momentum operator and psi is the position operator. I want to apply this to network dynamics but not sure how. Nodes and links would need to be part of some probability distribution that changes.

In signals detection they look for anomalies is data. Information traveling outside the norm. That might be where I need to start.

 

[dr froyd]

have you looked into bayesian networks

i have never used them but they seem kinda interesting

 

[fluffy]

Bayes theorem is about finding the probability of something when you have a smaller sample then to total set. Then updating the beliefs one has based on new data.

Prediction starts with a set of variables and their relationships. Probability is 1 > v(x) > 0

So you add up the evidence, multiply them and you get a percentage table. Sometimes you don't have all the data or need to calculate with limited variables in mind.

This works different in a network but is the same concept.

This is also effected by the new distribution created by increasing the size of the sample.

The increase my need to add new symbols for variable representation. Or just a new matrix size.