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In this Signal Processing ToolKit post, we examine the concept of a *random variable*.

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# Tag: Probability Density Function

## SPTK: Random Variables

## Are Probability Density Functions “Engineered” or “Hand-Crafted” Features?

Cyclostationary Signal Processing

Understanding and Using the Statistics of Communication Signals

Our toolkit expands to include basic probability theory.

Previous SPTK Post: Complex Envelopes Next SPTK Post: Examples of Random Variables

In this Signal Processing ToolKit post, we examine the concept of a *random variable*.

The Machine Learners think that their “feature engineering” (rooting around in voluminous data) is the same as “features” in mathematically derived signal-processing algorithms. I take a lighthearted look.

One of the things the machine learners never tire of saying is that their neural-network approach to classification is superior to previous methods because, in part, those older methods use *hand-crafted features*. They put it in different ways, but somewhere in the introductory section of a machine-learning modulation-recognition paper (ML/MR), you’ll likely see the claim. You can look through the ML/MR papers I’ve cited in The Literature ([R133]-[R146]) if you are curious, but I’ll extract a couple here just to illustrate the idea.