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Home » Applied Sciences » Computer Science » Probability Stochastic Process (V-U) » Set Theory (V-U)

Set Theory (V-U)

Course:Probability Stochastic Process (V-U)
Discipline:Applied Sciences
Institute:Virtual University
Instructor(s): Dr. Muhammad Akmal Butt
Level:Graduate

Probability Stochastic Process (V-U)

Asymptotic behaviour of Markov chains (V-U)
Basics of Markov chains, Examples of Markov chains (V-U)
Bayes Rule Application, Partitioned Footpath, Partioned Floor, Trains on a Junction (V-U)
Bipartite and tripartite graphs of Markov chains (V-U)
CDF of a Continuous RV, CDF and PDF of Nozzle Height, Exponential RV (V-U)
Moments of U and V from f(x, y) (V-U)
CDF of Poisson PMF, Joint Distribution Example, Marginal PMF (V-U)
Multiple derived RVs, Probability Computation (V-U)
Combinatronics, Birthday Problem, Bayes Rule (V-U)
n-step stochastic matrix, n-step reachability (V-U)
Conditional Distribution, Conditional PMF, Expected Value, Transformation of Random Variables (V-U)
Non-invertible Transformations (V-U)
Continuous Random Variable, Probability Density Function, PDF and CDF of a Continuous RV (V-U)
Norm of Vectors and Function, Convergence of RVs, Inequalities (V-U)
Continuous-time Markov chains, Poisson arrival and departure processes, Stationary probability distribution, Queuing theory (V-U)
Pair of derived RVs (V-U)
Convergence of Sequence, Convergence of RVs (V-U)
Pair of RVs , Buffon's Needle, Exponential RV Pair, Pair of Erlang RVs (V-U)
Correlation & Covariance, Correlated Gaussian RVs (V-U)
Partitioning of Markov chains (V-U)

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