Probability and Statistics for Signals and Networks

I am responsible for this 40-hour lecture course under the Master program in Intelligent and Communicating Systems.

Course Content:

  • Discrete and continuous probability distributions.
  • Law simulations and experiment modeling.
  • Statistical parameters (mean, variance, skewness, kurtosis), estimators.
  • Conditional probabilities.
  • Probabilistic algorithms: Las Vegas, Monte Carlo, Markov Chains.
  • Hidden Markov Models, Viterbi algorithm.
  • Queueing theory and network simulation.

Location:

Room A478, CYU.