ENEM 741 Probability and Random Process

Credit 3
Discrete-time and the continuous-time cases. Basic concepts of random variables, random vectors, stochastic processes, and random fields. Common random processes including the white noise, Gaussian processes, Markov processes, Poisson processes, and Markov random fields. Moment analysis (including Karhunen- Loeve transform), the frequency-domain description, and linear systems applied to stochastic processes. Elements of estimation theory and optimal filtering including Wiener and Kalman filtering. Advanced topics in modern statistical signal processing such as linear prediction, linear models and spectrum estimation are discussed

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ENEM 741 Probability and Random Process

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ENEM 741 Probability and Random Process

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