Quote:

Originally Posted by

**RiotDerivative**
It is actually pretty relevant. Anytime you deal with distributions containing multiple variables, you will be dealing with multiple integrals. I see it everywhere especially in Bayesian statistics. If you do statistical computing, one of the main responsibilities is "optimization." When you have multiple variables in an analysis or simulation, all of the data points form a surface or multidimensional geometric figure that you must optimize on. This is common in statistical computing and machine learning.

The only time I have seen polar coordinates was in seeing a proof on creating random normally distributed values using only uniform random numbers. That was pretty cool.

As an applied math major, most of the "optimization" we have done - is mostly linear optimization. This doesn't require much knowledge of multivariate calc (or at least that in depth).

Lots of people think about the possible combination of points as "forming a surface" but nobody expects you to think of it in multidimensional space (other then to understand which kinds of algorithms you're using to solve the optimization function).

TL;DR: it's good to learn all the multivariate calc/higher math but you don't really NEED it to understand what you're doing (other than to fully understand the concept/proof behind certain things)