Stationarity in Stochastic Processes

Related lectures (30)

Page 3 of 3

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.

Introduces continuous-time stochastic processes and provides examples illustrating ergodicism.

Covers the main properties of time-frequency domain signals and their limitations.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Explores variance, covariance, and practical applications in statistics and probability.

Explores memorylessness in distributions, stationary processes, and estimation using MLE.

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.

Covers image processing basics, Fourier transformation, convolution, cross-correlation, and 3D reconstruction using the Radon projection theorem.

Covers random vectors, stochastic models, functions, matrices, and expectations in communication systems.