1. Chapter 5 Statistical Models in Simulation

2. Basic Probability Theory Concepts
Discrete random variables
• Continuous random variables
• Cumulative distribution function
• Expectation

10. Discrete Distributions
Discrete random variables are used to describe random phenomena
in which only integer values can occur.
• Bernoulli trials and Bernoulli distribution
• Binomial distribution
• Geometric distribution
• Poisson distribution

11. -N Bernoulli trials where trials are independent
-Each trial has only two possible outcomes: S or F and
-Prob of success remains constant for each trial.

16. Models the no of independent events that occur in some fixed amt of time or space

18. Summary: Discrete Probability Distributions
Binomial Distribution: Models No. of successes in n bernoulli trials
Geometric Distribution: Models no. of Bernoulli trials to achieve 1st success
Negative Binomial Distribution: Models no. of Bernoulli trials to achieve kth success.
Poisson Distribution: Models no. of independent events that occur in fixed amount of time or space.

19. Continuous Distributions
Continuous random variables can be used to describe random
phenomena in which the variable can take on any value in some interval.
• Uniform
• Exponential
• Weibull
• Normal
• Lognormal

20. cdf
pdf

22. Exponential Distribution
It describes the time between the events in a poisson process.
Used to model the IATs when the arrivals are completely random and to model STs that are highly variable.
It can be used to model situations where certain events occur with a constant probability per unit length.
In the Queuing theory, it can be used to model STs of agents in the system.
It can also be used to model lifetime of the component that fails catastrophically .

23. Proof=?

25. Gamma Distribution
A random variable X is gamma distributed with parameters β and θ if its pdf is given by
= , Otherwise
cdf of X is given by
Fig. Pdf with θ =1 and β=1,2,3…….
Mean and Variance of Gamma Distribution are given by

26. The gamma distribution is frequently a probability model for waiting times
Notation: X~┌(β, θ) or X~gamma(β, θ)
Fig. Pdf with θ =1 and β=1,2,3…….

27. If β is an integer Gamma Distribution is related to Expo. Distri.:
If X=X1+X2+……..+Xβ
Where pdf of Xj is given by
and Xj are mutually independent, then X has pdf of Gamma Distribution.
When β =1 => Expo distri results.

28. Erlang Distribution Erlang Distribution:
The pdf of Gammma Distri is called Erlang Distri of order k when β =k , an integer.
E(X)=E(X1) +E(X2)+………+E(Xk) =
Mode= (k-1) / (kθ )
The cdf of Erlang distributed random variable X is given by
Reliability Function: Probability that system will operate for at least x hours R(x) = 1- F(x)

29. Triangular Distribution
Models a process when only the min,most likely and max. values of the distribution are known.
A Random variable X has a triangular distribution if its pdf is given by
where a ≤ b ≤ c.
Mean and Mode are computed as:
cdf is given by
, Otherwise

30. Weibull Distribution Models time to failure for the component.
When v=0 and β =1 Weibull distribution is reduced to Exponential distribution with parameter λ =1/α
The mean and variance of Weibull distribution are given by
Weibull Distribution
Models time to failure for the component.
A random variable X has a Weibull distribution if its pdf has the form
The cdf of Weibull distribution is given by

31. Figure Weibull probability density functions for selected values of  and .

32. Models the process that can be thought as the sum of a number of component processes. E.g. Time to assemble a product

35. Models a process that can be thought of as a product of a number of component processes. E.g. Rate of return on the investment.

41. Empirical Distributions
A distribution whose parameters are the observed values in a sample of data.
• May be used when it is impossible or unnecessary to establish that a
random variable has any particular parametric distribution.

42. Table: Arrivals per party distribution
Fig. Histogram of the party size
Fig. Empirical cdfof party size

43. Useful Statistical Models
• Queueing systems
• Inventory and supply-chain systems
• Reliability and maintainability
• Limited data