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stochastic process - think of the word "random", and "statistics". It's basically a collection of variables, all represented by time. Contrast stochastic processes with ODEs. ODEs are not random processes, they are systematic, where if you have an initial condition, you will ALWAYS get the SAME answer when you plug and chug the equations when solving the equation when t = a certain value in time. So, stochastic processes are where you have these system of variables indexed by time, where if you have an initial condition, will not be the same each time you plug and chug.

Bayes Theorem - Famous theorem/equation in statistics about "conditional probability". Let's say you have a variable (e.g. if it is raining outside today), with different mutually exclusive states/events (e.g. "it is raining" or "it is not raining"). With Bayes theorem, we can determine if another variable and its events (e.g. if a weather man says it will/will not be raining on that day) can correlate/cause/correspond to what happens with variable A. Useful in pretty much anything where you need to know if one thing causes/correlates/corresponds/associated to another thing.

contralateral - opposite side to the actual location of the "thing" in question. It's typically used in medical literature. e.g. "we are also trying to monitor the contralateral side of the infected joint"

Conditional probability - P(X|Y) = P(X ∩ Y)/P(Y). A simple
way to think about probabilities conditional upon Y is to imagine that the universe of
events U has shrunk to Y. The conditional probability of X on Y is just the measure
of what is left of X relative to what is left of Y

disposition - someone's inherent nature/quality/characteristics

disposition - someone's inherent nature/quality/characteristics

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