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The common Philidor pulls in lots of different concepts from different areas of mathematics, so we're

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going to look at a different building blocks of the common future and we're going to do them in order.

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So this is going to be our learning roadmap that we're going to use to learn the common filter.

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First off, we're going to start with probability and uncertainty.

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So we're going to learn how to mathematically represent different levels of uncertainty using probabilistic

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concepts.

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From there, we're going to look at linear dynamic systems.

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The common filter has a concept of dynamic systems, and it's going to use this dynamic system for the

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estimation process.

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So this describes what we're actually trying to estimate inside the common filter.

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Now, we also look at least squares estimation and the estimation pulls in the probability concepts

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into the estimation process.

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And then using these three above concepts of probability least squares and dynamic systems, we're going

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to form it into a single concept for the linear common filter.

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From here, we're going to look at the implementation methods and practical considerations of using

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the common filter in real life.