φ_T[f] = ∫ dt p(t) exp(-2πift) = ⟨ exp(-2πifT) ⟩
If two variables X ~ r and Y ~ s are independent then you can prove [from ⟨f(X) g(Y)⟩ = ⟨f(X)⟩ ⟨g(Y)⟩ or X+Y ~ q where q(z) = ∫ dx r(x) s(z — x)] that their sum has a characteristic function
φ_{X+Y} = φ_X + φ_Y
And therefore the “sample mean” M of n IID variables is itself a random variable with characteristic function
φ_M[f] = ( φ[f/n] )^n.
So we find that
log φ_M[f] = n log φ[f/n] ≈ 0 + i a f – b f²/n + O(f³/n²).
These terms [a, b] from expanding the log of the characteristic function constitute the cumulant expansion and for large n the other terms shrink to zero, so that the characteristic function is to first order in 1/n a Gaussian.
The characteristic function was a Fourier transform of a PDF, so an inverse Fourier transform gets it back:
p(t) = ∫ df φ_T[f] exp(2πift)
But the Fourier transform of a Gaussian is just a Gaussian.
So a B would be 70 to 85.
This makes the grades unfair and not useful for judging mastery of the subject. It only makes sense if the goal of a course is to beat the other students. For most courses, the goal should be learning.
90%+ => A (4.0)
80%+ => B (3.0)
70%+ => C (2.0)
60%+ => D or F depending (1.0 or 0.0)
At university, we only used grade points and they were very, very similar in scale.At my university the letter grades were
A+ 99.00 - 100.00%
A 93.00 - 98.99%
A- 90.00 - 92.99%
B+ 87.00 - 89.99%
B 83.00 - 86.99%
B- 80.00 - 82.99%
C+ 77.00 - 79.99%
C 73.00 - 76.99%