18 episodes

Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. Will review the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course.

Statistical Signal Processing UC Santa Cruz

    • Technology

Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. Will review the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course.

    • video
    Overview and Review

    Overview and Review

    • 1 hr 22 min
    • video
    Random Processes

    Random Processes

    • 1 hr 31 min
    • video
    Chi-squared distribution

    Chi-squared distribution

    • 1 hr
    • video
    Binary Hypothesis Testing

    Binary Hypothesis Testing

    • 1 hr 37 min
    • video
    Bayes Risk

    Bayes Risk

    • 1 hr 24 min
    • video
    Matched Filter

    Matched Filter

    • 1 hr 24 min

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