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from Part I - Estimation Machinery
Published online by Cambridge University Press: 11 January 2024
As the book attempts to be as stand-alone as possible, this chapter provides up front a summary of all the results in probability theory that will be needed later on. Probability is key to estimation as we not only want to estimate, for example, where something is but how confident we are in that estimate. The first half of the chapter introduces general probability density functions, Bayes' theorem, the notion of independence, and quantifying uncertainty amongst other topics. The second half of the chapter delves into Gaussian probability density functions specifically and establishes the key tools needed in common estimation algorithms to follow in later chapters. This chapter can also simply serve as a reference for readers already familiar with the content.
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