The common Philidor pulls in lots of different concepts from different areas of mathematics, so we're
going to look at a different building blocks of the common future and we're going to do them in order.
So this is going to be our learning roadmap that we're going to use to learn the common filter.
First off, we're going to start with probability and uncertainty.
So we're going to learn how to mathematically represent different levels of uncertainty using probabilistic
concepts.
From there, we're going to look at linear dynamic systems.
The common filter has a concept of dynamic systems, and it's going to use this dynamic system for the
estimation process.
So this describes what we're actually trying to estimate inside the common filter.
Now, we also look at least squares estimation and the estimation pulls in the probability concepts
into the estimation process.
And then using these three above concepts of probability least squares and dynamic systems, we're going
to form it into a single concept for the linear common filter.
From here, we're going to look at the implementation methods and practical considerations of using
the common filter in real life.