# Statistical Signal Processing, Lecture

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```					Statistical Signal Processing, Lecture
This list represents typical questions you might be asked during the exam. However, there is
no guarantee that you will be asked any of the provided questions!

Which class of problems is covered by the topic „Estimation Theory“?

Describe the following terms: unbiased/efficient/consistent/minimum variance estimator

What is a Random Variable (Sketch, explanation)?

Explain the Neyman-Pearson theorem. How can it be applied?

What is the CRLB? Use the „constant in AWGN“ example to explain the steps involved in
the derivation.

Explain the ROC of a binary detector.

Explain the Wiener-Khinchin theorem.

What is he BLUE? When is it used? What are the advantages/disadvantages with respect to
the MVUE obtained using the CRLB theorem?

Outline a binary detection problem. Setup the signal model and sketch the appropriate
likelihood functions. Clearly identify the possible errors.

How can you characterise and classify a Random Process?

How can you determine the outcome of a Random Process being passed through an LTI
system?

Explain the mapping of Random Variables using an arbitrary scalar-valued function g(.)

Explain the differences between classical and Bayesian estimation theory.

What is an AR(3) process?

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