Engineering math course in the first year of undergraduate would cover vector calculus. Jacobian and Hessian matrices are naturally covered. Jacobian matrix of a vector-valued function is
A use of the Jacobian is when , the matrix is square and its determinant is used for transformation of coordinate system. For example, a function defined on the Cartesian coordinate system in 2D, , represented in polar coordinates becomes . The coordinate transformation is indeed a function
and the Jacobian determinant is .
If we consider the integral of over a region on the Cartesian coordinate system, it can be transformed into polar coordinate using Jacobian:
This means the infinisimal area , i.e., the area is uneven and depends on in polar coordinate. Math.StackExchange has an article on the proof of multivariable change of variable formula to explain this in the general coordinate system transform in integration. So, similarly if we apply the same to spherical coordinates ,
and we have .
With this, we can check the coefficient of the normal density function : Consider , and and converting Cartesian coordinate into polar, we have
Therefore the normal density function should be . This is just one way to evaluate. See, for example, Feynman’s differentiation under integral sign technique against (also another example) for a different approach to evaluate this Gaussian integral.
Hessian matrix is the second derivative, ,
This arises if we take the Taylor expansion of a vector function, in the coefficient of the quadratic term:
as well as the expansion of the gradient of a vector function:
Therefore, we can expect to encounter Jacobian more often. Indeed we used it in the post about Newton method in vector functions.
Feynman’s differentiation under integral sign can use to attack a variation of Gaussian integrals, e.g., with cosine transform. See Conrad for detail.