The Kalman Filter II: Conditioning
We derive a useful formula that allows us to compute the conditional expectation of jointly normally distributed data; this result plays a central role in the development of the Kalman filter
We derive a useful formula that allows us to compute the conditional expectation of jointly normally distributed data; this result plays a central role in the development of the Kalman filter
We present the Gauss-Markov Model, thus taking our first step towards understanding the Kalman filter
Dual of p-norm via the KKT theorem
An introduction to polynomial chaos expansions
From the Hahn-Banach theorem to the three separating theorems
From the Hahn-Banach theorem to the three separating theorems
Table of Itô stochastic integrals and their variances
Some notes on the differentiability of the pointwise max of a family of functions and the Fritz-John and Mangasarian-Ffromowitz optimality conditions
Useful convex analysis stuff
Some notes on the Rayleigh quotient