Added exercises comparing 1D filter using Gaussians vs 1D filter
using KalmanFilter class, and comparing a 1D filter vs a filter
incorporating the unobserved derivative of x.
I originally used sense/update as my function names. But all the
literature uses update/predict, so I am normalizing on that.
I also wrote a test to ensure the update/predict functions in the
1d kalman filter chapter gives the same output as the full blown
kalman filter. Of course, they do. This is in the 1d_kf_compare
notebook. I will add this as an exercise in the multidimensional KF
chapter.
Started developing EKF chapter. Stalled - not sure what is the best
initial example to develop. Air drag seems unnecessarily 'mathy',
but then you need diff eq to compute Jacobians.
