**Introduction to stochastic process** Dr. NurAiniMasruroh

**Outlines ** Concept of probability Random variables Expected value Conditional probability Limit theorem Stochastic processes

**Probability in Industrial Engineering** • Random arrivals of jobs • Random service time • Random requests for resources • Probability of queue overflow • Service delays • Scheduling problems • Flow control and routing • Revenue management • Risk assessment for decision analysis • Etc

**Basic probability** • Random experiment: an experiment whose outcome cannot be determined in advanced • Sample space (S): set of all possible outcomes • Event (E): a subset of sample space, it occurs if the outcome of the experiment is an element of that subset • A number P(E) is defined and satisfies: • 0 ≤ P(E) ≤ 1 • P(S) =1 • For any sequence of events E1, E2, … that are mutually exclusive

**Random variables** • Consider a random experiment having sample space S. • A random variable X is a function that assigns a real value to each outcome in S • The distribution function F of the random variable X is defined for any real number x by • A random variable X is said to be discrete if its set of possible values is countable. For discrete random variables, • A random variable is called continuous if there exists a function f(x), called the probability density function, such that For every set B

**Random variables** • Since it follows that • The join distribution function F of two random variables X and Y is defined by F(x, y) = P{X≤ x, Y ≤ y} • The distribution functions of X and Y, Fx(x) = P{X≤ x} and FY(y) = P{Y≤y} It can be obtained from F(x, y) by making use of the continuity property of the probability operator. Continuity property: Similarly, F(x, y) = Fx(x)FY(y) if X and Y are independent

**Cumulative Distribution Function (CDF)** • CDF, F, of the random variable X is defined by all real numbers b, -∞ < b < ∞, by F(b) = P(X ≤ b) • F(b) denotes the cumulative probability that the random variable X takes on a value that is less or equal to b (the total probability mass) • CDF is a non-decreasing function: P(X≤ a) ≤ P(X ≤ b) if a < b and thus F(a) ≤ F(b) for a < b • F(∞) = 1 and F(-∞)=0: bounded • CDF is a right continuous function: for any b, F(b+), value of F(b) just to the right of b, equals to F(b) as b+ get closer to b. The CDF is right continuous at all values, but may be left discontinuous at some values

**Probabilities from CDF** • P(X ≤ b) = F(b) • P(X<b) = F(b – ε) = F(b-) • ε is a small number • P(X=b) = P(X ≤ b) – P(X < b) = F(b) – F(b-) • P(a < X ≤ b) = F(b) – F(a) • P(a ≤ X ≤ b) = F(b) – F(a-) • P(a ≤ X < b) = F(b-) – F(a-) • P(a < X < b) = F(b-) – F(a)

**Probability Density Function (pdf)** • The pdf of a continuous random variable X is defined by the derivative of the continuous CDF at differentiable intervals : dF(b)/db = f(b) • The pdf of a continuous random variable tells us the density of the mass at each point on the sample space • The value of pdf is not probability, for example, if f(x) = 2x, 0 ≤ x ≤ 1, and 0 otherwise, f(1) = 2 is obviously not a probability value • Graphically, pdf is the area under f(x) from ain interval a,b. • Note: if pmf has meaning itself, the value itself for pdf has no meaning

**Discrete random variable: summary** • A random variable X associates a number with each outcome of an experiment • A discrete random variable takes on a finite number • The probabilistic behavior of a discrete random variable X is described by its probability mass function (pmf), p(u) • P(uj) = p(X=uj) • All the probabilistic information about the discrete random variable X is summarized in its pmf • A discrete random variable X has a CDF F(b) which is: • Right-continuous • Staircase CDF

**Continuous random variable: summary** • No mass to define pmf: every event has zero probability • Example: p(height of people= 173.7897654) 0 • However P(147< height of people < 187) 1 • The set of possible values are uncountable while the set of possible values were finite or countably infinite for a discrete random variable • Sample space is not a discrete set, but a continuous space (or interval) • A continuous random variable X has a CDF F(b) which is: • Continuous at all b, -∞ < b < ∞ • Differentiable at all b (except possibly at finite set of points)

**Expected value** • Expectation of the random variable X, • The variance of the random variable X is defined by Var X = E[(X – E[X])2] = E[X2] – E2[X] • Two jointly distributed random variables X and Y are said to be uncorrelated if their covariance defined by Cov (X, Y) = E[(X – EX)(Y – EY)] = E[XY] – E[X]E[Y]

**Expected value** • Expectation of a sum of random variables is equal to the sum of the expectations • Variances:

**Conditional probabilities** • In general, given information about the outcome of some events, we may revise our probabilities of other events • We do this through the use of conditional probabilities • The probability of an event X given specific outcomes of another event Y is called the conditional probability X given Y • The conditional probability of event X given event Y and other background information ξ, is denoted by p(X|Y, ξ) and is given by

**Bayes’ Theorem** • Given two uncertain events X and Y. Suppose the probabilities p(X|ξ) and p(Y|X, ξ) are known, then

**Factorization rule for joint probability**

**Limit theorem ** • Strong Law of Large Numbers • If X1, X2, … are independent and identically distributed with mean μ, then • Central Limit Theorem • If X1, X2, … are independent and identically distributed with mean μ and variance σ2, then • Thus if we let where X1, X2, … are independent and identically distributed, then the Strong of LLN states that, with probability1, Sn/n will converge to E[Xi]; whereas the CLT states that Sn will have an asymptotic normal distribution as n → ∞

**Stochastic process** X(t) is the state of the process (measurable characteristic of interest) at time t • the state space of the a stochastic process is defined as the set of all possible values that the random variables X(t) can assume • when the set T is countable, the stochastic process is a discrete time process; denote by {Xn, n=0, 1, 2, …} • when T is an interval of the real line, the stochastic process is a continuous time process; denote by {X(t), t≥0}

**Stochastic process** Hence, • a stochastic process is a family of random variables that describes the evolution through time of some (physical) process. • usually, the random variables X(t) are dependent and hence the analysis of stochastic processes is very difficult. • Discrete Time Markov Chains (DTMC) is a special type of stochastic process that has a very simple dependence among X(t) and renders nice results in the analysis of {X(t), t∈T} under very mild assumptions.

**Example of stochastic processes** Refer to X(t) as the state of the process at time t A stochastic process {X(t), t∈T} is a time indexed collection of random variables • X(t) might equal the total number of customers that have entered a supermarket by time t • X(t) might equal the number of customers in the supermarket at time t • X(t) might equal the stock price of a company at time t

**Counting process** • Definition: • A stochastic process {N(t), t≥0} is a counting process if N(t) represents the total number of “events” that have occurred up to time t

**Counting process ** • Examples: • If N(t) equal the number of persons who have entered a particular store at or prior to time t, then {N(t), t≥0} is a counting process in which an event corresponds to a person entering the store • If N(t) equal the number of persons in the store at time t, then {N(t), t≥0} would not be a counting process. Why? • If N(t) equals the total number of people born by time t, then {N(t), t≥0} is a counting process in which an event corresponds to a child is born • If N(t) equals the number of goals that Ronaldo has scored by time t, then {N(t), t≥0} is a counting process in which an event occurs whenever he scores a goal

**Counting process** • A counting process N(t) must satisfy • N(t)≥0 • N(t) is integer valued • If s ≤t, then N(s) ≤ N(t) • For s<t, N(t)-N(s) equals the number of events that have occurred in the interval (s,t), or the increments of the counting process in (s,t) • A counting process has • Independent increments if the number of events which occur in disjoint time intervals are independent • Stationary increments if the distribution of the number of events which occur in any interval of time depends only on the length of the time interval

**Independent increment** • This property says that numbers of events in disjoint intervals are independent random variables. • Suppose that t1< t2≤ t3< t4. Then N(t2)-N(t1), the number of events occurring in (t1,t2], is independent of N(t4)-N(t3), the number of events occurring in (t3, t4].