Introduction to Probability

A₁ approximation arrival assume balance equations Bernoulli process calculate conditional PMF conditional probabilities Consider continuous random variable converges corresponding defined denoted discrete random variable disjoint events E[X² E[XY end-of-chapter problems equal Example expected value experiment exponential random variable exponentially distributed Figure finite formula function fx(x geometric given independent random variables infinite number integer interarrival interval joint PMF large number law of large linear marginal PMFs Markov chain multiplication rule nonnegative normal random variables number of elements number of possible obtain p₁ packets permutations PMF px Poisson PMF Poisson process possible outcomes probabilistic model probability law PX,Y px(x result roll sample space sequence sequential Show slots Solution steady-state probabilities student subset success T₁ total expectation theorem total number total probability theorem transform associated transient var(X variable with parameter X₁ zero