CATALOG DESCRIPTION: Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson ...

Applications range from medical imaging to autonomous vehicle technology. Learn data manipulation techniques to improve signal or image fidelity. Understand the theory of probability and stochastic ...

Ivan Bajic (ibajic at ensc.sfu.ca) Office hours: Monday and Wednesday, 13:00-14:00 online (Zoom, see the link in course materials) Introduction to the theories of probability and random variables, and ...

Explain why probability is important to statistics and data science. See the relationship between conditional and independent events in a statistical experiment. Calculate the expectation and variance ...

Stochastic analysis and modelling encompasses the formulation, characterisation and computation of dynamic systems subject to intrinsic randomness or external noise. At its core lie stochastic ...

French mathematician and astronomer, Pierre-Simon Laplace brought forth the first major treatise on probability that combined calculus and probability theory in 1812. A single roll of the dice can be ...

Branching processes in random environments (BPREs) extend classical Galton–Watson models by allowing the reproduction law to vary according to a sequence of random factors. In each generation, the ...

CATALOG DESCRIPTION: Advanced topics in random processes: point processes, Wiener processes; Markov processes, spectral representation, series expansion of random processes, linear filtering, Wiener ...