© Robert J. Marks II ENGR 5345 Review of Probability & Random Variables.

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© Robert J. Marks II ENGR 5345 Review of Probability & Random Variables

© Robert J. Marks II Random Variables Assign each event outcome in S to a real number (random variable), X. Ex:heads  X=12 tails  X=47 If the event outcome is numerical, equating the outcome to X is often convenient.

© Robert J. Marks II Random Variables (cont) Each RV, X, has a probability equal to the event to which it is assigned. The Cumulative Distribution Function (CDF)

© Robert J. Marks II CDF Properties 1. Since the CDF is a probability, 2. The CDF is monotonically increasing (a.k.a. nondecreasing) Note:, 1 x

© Robert J. Marks II CDF Properties (cont) 3. The CDF is continuous from the right 1 x

© Robert J. Marks II Probabilities from CDF’s x ba p

© Robert J. Marks II Probability Density Function

© Robert J. Marks II PDF Properties fX(x)fX(x) x A

© Robert J. Marks II Histograms as PDF’s A histogram normalized to unit area is an empirical pdf. N = # data points hN(x)hN(x) x 

© Robert J. Marks II Conditional CDF’s

© Robert J. Marks II Conditional pdf’s

© Robert J. Marks II Discrete RV’s Discrete RV’s have only integer outputs. impulse function (Dirac delta) Probability mass function (pmf) Beware of deltas on the edge of integration when evaluating

© Robert J. Marks II Common RV PDF’s Bernoulli, p = probability of success Jacob Bernoulli Born: 27 Dec 1654 in Basel, Switzerland Died: 16 Aug 1705 in Basel, Switzerland fX(x)fX(x) x 10 p q=1-p Pictures in this presentation from

© Robert J. Marks II Common RV PDF’s Binomial ( n repeated Bernoulli trials) = binomial coefficient (Pascal’s triangle)

© Robert J. Marks II Binomial RV ( p =0.1) vs. n k n

© Robert J. Marks II Binomial RV ( p =0.2) vs. n k n

© Robert J. Marks II Binomial RV ( p =0.5) vs. n k n

© Robert J. Marks II Geometric RV Repeat a Bernoulli trial until a success occurs. The number of trials is a geometric random variable.

© Robert J. Marks II Negative Binomial (Pascal) RV Repeat Bernoulli trials. Let X =number of trials to achieve r th success. Blaise Pascal Born: 19 June 1623 in Clermont France Died: 19 Aug 1662 in Paris, France

© Robert J. Marks II Pascal Blaise Pascal ( ) PASCAL:PASCAL: a high level programming language designed by Niklaus Wirth in 1974 as a teaching language for computer scientists. Pascal’s Law:Pascal’s Law: the pressure in a fluid is transmitted equally to all distances and in all directions. PASCAL:PASCAL: A unit of pressure. 1 bar equals 100,000 Pascal Pascal’s triangle.Pascal’s triangle. ( )

© Robert J. Marks II Pascal Pascal: Computer Engineer In 1642, Pascal began to create a machine that would be similar to an everyday calculator to help his father with his accounting job. In 1642, Pascal began to create a machine that would be similar to an everyday calculator to help his father with his accounting job. He finished the final model in 1645.He finished the final model in He presented one to Queen Christina of Sweden and he was allowed a monopoly over it by royal decree.He presented one to Queen Christina of Sweden and he was allowed a monopoly over it by royal decree.

© Robert J. Marks II Pascal’s Wager… Pascal, a Christian theist still studied in philosophy and religion, communicated theological concepts wrapped in the interest of his contemporaries’ interest in probability and gambling. "Let us examine this point and say, "God is, or He is not."... What will you wager?... Let us weigh the gain and loss of wagering that God is... there is here an infinity of an infinitely happy life to gain, a chance of gain against a finite number of chances of loss, and what you stake is finite... every player stakes a certainty to gain an uncertainty, and yet he stakes a finite certainty to gain a finite uncertainty, without transgressing against reason... the uncertainty of the gain is proportioned to the certainty of the stake according to the proportion of the chances of gain and loss.... And so our proposition is of infinite force, when there is the finite to stake in a game where there are equal risks of gain and loss, and the infinite to gain.” Article 223 of Pensees

© Robert J. Marks II Poisson RV Number of random points on a line segment. Approximation of binomial random variable. Siméon Denis Poisson Born: 21 June 1781 in Pithiviers, France Died: 25 April 1840 in Sceaux, France

© Robert J. Marks II Discrete Uniform RV Describes die RV with a =1 and b =6. Round of money to the nearest dollar results in an error that is uniform & discrete.

© Robert J. Marks II Continuous Uniform RV Describes random angle with a=0 and b=2  Rounding & truncation errors

© Robert J. Marks II Gaussian (normal) RV Limiting distribution in Central Limit Theorem 1803 Johann Carl Friedrich Gauss Born: 30 April 1777 in Brunswick, Duchy of Brunswick (Germany) Died: 23 Feb 1855 in Göttingen, Hanover (Germany)

© Robert J. Marks II Gaussian (normal) RV 22 fX(x)fX(x) x

© Robert J. Marks II Gaussian (normal) CDF

© Robert J. Marks II Gaussian Error Function

© Robert J. Marks II Matlab erf ERF Error function. Y = ERF(X) is the error function for each element of X. X must be real. The error function is defined as: erf(x) = 2/sqrt(pi) * integral from 0 to x of exp(-t^2) dt. Conversion Formula?

© Robert J. Marks II Exponential RV  (x)= unit step Commonly used model in reliability for constant failure rates.

© Robert J. Marks II The Ageless Exponential RV 1. Let X be how long you live after you’re born and 2. Thus: 3. You’ve lived y years. 4. Good as NEW!

© Robert J. Marks II Cauchy RV Augustin Louis Cauchy Born: 21 Aug 1789 in Paris, France Died: 23 May 1857 in Sceaux, France “Loose Cannon” RV Ratio of two Gaussians is Cauchy Displays strange properties - undefined mean and  second moment.

© Robert J. Marks II Cauchy RV

© Robert J. Marks II Laplace RV Pierre-Simon Laplace Born: 23 March 1749 in Beaumont-en-Auge, Normandy, France Died: 5 March 1827 in Paris, France

© Robert J. Marks II Pierre Simon Laplace ( ). Namesakes: Laplace transformLaplace transform Laplace NoiseLaplace Noise Laplace helped to establish the metric system.Laplace helped to establish the metric system. Laplacian OperatorLaplacian Operator Napoleon appointed Laplace Minister of the Interior but removed him from office after only six weeks “because he brought the spirit of the infinitely small into the government.”

© Robert J. Marks II Gamma RV Gamma Function Erlang RV: Gamma for a=n

© Robert J. Marks II Other RV’s Weibull  2 (chi-squared) Wallodi Weibull

© Robert J. Marks II Other RV’s F Student’s t William Sealey Gosset a.k.a. Student t Born: 13 June 1876 in Canterbury, England Died: 16 Oct 1937 in Beaconsfield, England

© Robert J. Marks II Other RV’s Rice Raleigh Pareto

© Robert J. Marks II Other RVs Gamma Function Erlang RV: Gamma for a=n

© Robert J. Marks II Mixed Random Variables Part Continuous – Part Discrete. Example: A traffic light is red three minutes, then green two. You come on this traffic light. Let T = the time you need to wait at the light. T is a mixed random variable.

© Robert J. Marks II Traffic Light t