Generating random numbers

Transcription

1 Generating random numbers Lecturer: Dmitri A. Moltchanov

2 OUTLINE: Why do we need random numbers; Basic steps in generation; Uniformly distributed random numbers; Statistical tests for uniform random numbers; Random numbers with arbitrary distributions; Statistical tests for random numbers with arbitrary distribution; Multidimensional distributions. Lecture: Generating random numbers 2

3 1. The need for random numbers Examples of randomness in telecommunications: interarrival times between arrivals of packets, tasks, etc.; service time of packets, tasks, etc.; time between failure of various components; repair time of various components;... Importance for simulations: random events are characterized by distributions; simulations: we cannot use distribution directly. For example, M/M/1 queuing system: arrival process: exponential distribution with mean 1/λ; service times: exponential distribution with mean 1/µ. Lecture: Generating random numbers 3

4 Discrete-event simulation of M/M/1 queue INITIALIZATION time:=0; queue:=0; sum:=0; throughput:=0; generate first interarrival time; MAIN PROGRAM while time < runlength do case nextevent of arrival event: time:=arrivaltime; add customer to a queue; start new service if the service is idle; generate next interarrival time; departure event: time:=departuretime; throughput:=throughtput + 1; remove customer from a queue; if (queue not empty) sum:=sum + waiting time; start new service; OUTPUT mean waiting time = sum / throughput Lecture: Generating random numbers 4

5 2. General notes General approach nowadays: transforming one random variable to another one; as a reference distribution a uniform distribution is often used. Note the following: most simulators contain generator of uniformly distributed numbers in interval (0, 1). may not contain arbitrarily distributed random numbers you want. The procedure is to: generate RN with inform distribution between a and b, b >>>> a; transform it somehow to random number with uniform distribution on (0, 1); transform it somehow to a random number with desired distribution. Lecture: Generating random numbers 5

6 2.1. Pseudo random numbers All computer generated numbers are pseudo ones: we know the method how they are generated; we can predict any random sequence in advance. The goal is then: imitate random sequences as good as possible. Requirements for generators: must be fast; must have low complexity; must have sufficiently long cycles; must allow to generate repeatable sequences; must be independent; must closely follow a given distribution. Lecture: Generating random numbers 6

7 2.2. Step 1: uniform random numbers in (a, b) Basic approach: generate random number with uniform distribution on (a, b); transform these random numbers to (0, 1); transform it somehow to a random number with desired distribution. Uniform generators: old methods: mostly based on radioactivity; Von Neumann s algorithm; congruential methods. Basic approach: next number is some function of previous one γ i+1 = F (γ i ), i = 0, 1,..., (1) recurrence relation of the first order; γ 0 is known and directly computed from the seed. Lecture: Generating random numbers 7

8 2.3. Step 2: transforming to random numbers in (0, 1) Basic approach: generate random number with uniform distribution on (0, 1); transform these random numbers to (0, 1); transform it somehow to a random number with desired distribution. Uniform U(0, 1) distribution has the following pdf: 1, 0 x 1 f(x) = 0, otherwise. (2) Lecture: Generating random numbers 8

9 Mean and variance are given by: E[X] = 1 0 xdx = x = 1 2, σ 2 [X] = (3) How to get U(0, 1): by rescaling from U(0, m) as follows: y i = γ i /m, (4) where m is the modulo in linear congruential algorithm. What we get: something like: 0.12, 0.67, 0.94, 0.04, 0.65, 0.20,... ; sequence that appears to be random... Lecture: Generating random numbers 9

10 2.4. Step 3: non-uniform random numbers Basic approach: generate random number with uniform distribution on (a, b); transform these random numbers to (0, 1); transform it somehow to a random number with desired distribution. If we have generator U(0, 1) the following techniques are avalable: discretization: bernoulli, binomial, poisson, geometric; rescaling: uniform; inverse transform: exponential; specific transforms: normal; rejection method: universal method; reduction method: Erlang, Binomial; composition method: for complex distributions. Lecture: Generating random numbers 10

11 3. Uniformly distributed random numbers The generator is fully characterized by (S, s 0, f, U, g): S is a finite set of states; s 0 S is the initial state; f(s S) is the transition function; U is a finite set of output values; g(s U) is the output function. The algorithm is then: let u 0 = g(s 0 ); for i = 1, 2,... do the following recursion: s i = f(s i 1 ); u i = g(s i ). Note: functions f( ) and g( ) influence the goodness of the algorithm heavily. Lecture: Generating random numbers 11

12 user choice s 0 u 0 =g(s 0 ) u 0 u 4 s 1 =f(s 0 ) s 1 s 0 s 3 s 4 s 4 =f(s 3 ) u 1 =g(s 1 ) u 4 =g(s 4 ) u 3 =g(s 3 ) u 1 u 3 s 2 =f(s 1 ) s 3 =f(s 2 ) s 2 u 2 u 2 =g(s 2 ) Figure 1: Example of the operations of random number generator. Here s 0 is a random seed: allows to repeat the whole sequence; allows to manually assure that you get different sequence. Lecture: Generating random numbers 12

13 3.1. Von Neumann s generator The basic procedure: start with some number u 0 of a certain length x (say, x = 4 digits, this is seed); square the number; take middle 4 digits to get u 1 ; repeat... example: with seed 1234 we get 1234, 5227, 3215, 3362, 3030, etc. Shortcoming: sensitive to the random seed: seed 2345: 2345, 4990, 9001, 180, 324, 1049, 1004, 80, 64, (will always < 100); may have very short period: seed 2100: 2100, 4100, 8100, 6100, 2100, 4100, 8100,... (period = 4 numbers). To generate U(0, 1): divide each obtained number by 10 x (x is the length of u 0 ). Note: this generator is also known as midsquare generator. Lecture: Generating random numbers 13

14 3.2. Congruential methods There are a number of versions: additive congruential method; multiplicative congruential method; linear congruential method; Tausworthe binary generator. General congruential generator: u i+1 = f(u i, u i 1,... ) mod m, (5) u i, u i 1,... are past numbers. For example, quadratic congruential generator: u i+1 = (a 1 u 2 i + a 2 u i 1 + c) mod m. (6) Note: if here a 1 = a 2 = 1, c = 0, m = 2 we have the same as midsquare method. Lecture: Generating random numbers 14

15 3.3. Additive congruential method Additive congruential generator is given: u i+1 = (a 1 u i + a 2 u i a k u i k ) mod m. (7) The common special case is sometimes used: u i+1 = (a 1 u i + a 2 u i 1 ) mod m. (8) Characteristics: divide by m to get U(0, 1); maximum period is m k ; note: rarely used. Shortcomings: consider k = 2: consider three consecutive numbers u i 2, u i 1, u i ; we will never get: u i 2 < u i < u i 1 and u i 1 < u i < u i 2 (must be 1/6 of all sequences). Lecture: Generating random numbers 15

16 3.4. Multiplicative congruential method Multiplicative congruential generator is given: u i+1 = (au i ) mod m. (9) Characteristics: divide by m to get U(0, 1); theoretical maximum period is m; note: rarely used. Shortcomings: can never produce 0. Choice of a, m is very important: recommended m = (2 p 1) with p = 2, 3, 5, 7, 13, 17, 19, 31, 61 (Fermat numbers); if m = 2 q, q 4 simplifies the calculation of modulo; practical maximum period is at best no longer than m/4. Lecture: Generating random numbers 16

17 3.5. Linear congruential method Linear congruential generator is given: u i+1 = (au i + c) mod m, (10) where a, c, m are all positive. Characteristics: divide by m to get U(0, 1); maximum period is m; frequently used. Choice of a, c, m is very important. To get full period m choose: m and c have no common divisor; c and m are prime number (distinct natural number divisors 1 and itself only); if q is a prime divisor of m then a = 1, mod q; if 4 is a divisor of m then a = 1, mod 4. Lecture: Generating random numbers 17

18 The step-by-step procedure is as follows: set the seed x 0 ; multiply x by a and add c; divide the result by m; the reminder is x 1 ; repeat to get x 2, x 3,.... Examples: x 0 = 7, a = 7, c = 7, m = 10 we get: 7,6,9,0,7,6,9,0,... (period = 4); x 0 = 1, a = 1, c = 5, m = 13 we get: 1,6,11,3,8,0,5,10,2,7,12,4,9,1... (period = 13); x 0 = 8, a = 2, c = 5, m = 13 we get: 8,8,8,8,8,8,8,8,... (period = 1!). Recommended values: a = 314, 159, 269, c = 453, 806, 245, m = 231 for 32 bit machine. Lecture: Generating random numbers 18

19 Complexity of the algorithm: addition, multiplications and division: division is slow: to avoid it set m to the size of the computer word. Overflow problem when m equals to the size of the word: values a, c and m are such that the result ax i + c is greater than the word; it may lead to loss of significant digits but it does not hurt! How to deal with: register can accommodate 2 digits at maximum; the largest number that can be stored is 99; if m = 100: for a = 8, u 0 = 2, c = 10 we get (au i + c) mod 100 = 26; if m = 100: for a = 8, u 0 = 20, c = 10 we get (au i + c) mod 100 = 170; au i = 8 20 = 160 causing overflow; first significant digit is lost and register contains 60; the reminder in the register (result) is: ( ) mod 70 = 70. the same as 170 mod 100 = 70. Lecture: Generating random numbers 19

20 3.6. How to get good congruental generator Characteristics of good generator: should provide maximum density: no large gaps in [0, 1] are produced by random numbers; problem: each number is discrete; solution: a very large integer for modulus m. should provide maximum period: achieve maximum density and avoid cycling; achieve by: proper choice of a, c, m, and x 0. effective for modern computers: set modulo to power of 2. Lecture: Generating random numbers 20

21 3.7. Tausworthe generator Tausworthe generator (case of linear congruential generator or order k): ( k ) z i = (a 1 z i 1 + a 2 z i a k z i k + c) mod 2 = a j z i j + c mod 2. (11) where a j {0, 1}, j = 0, 1,..., k; the output is binary: j=1 Advantages: independent of the system (computer architecture); independent of the word size; very large periods; can be used in composite generators (we consider in what follows). Note: there are several bit selection techniques to get numbers. Lecture: Generating random numbers 21

22 A way to generate numbers: choose an integer l k; split in blocks of length l and interpret each block as a digit: u n = l 1 j=0 z nl+j 2 (j+1). (12) In practice, only two a i are used and set to 1 at places h and k. We get: Example: h = 3, k = 4, initial values 1,1,1,1; we get: ; period is 2 k 1 = 15; if l = 4: 13/16, 7/16, 8/16, 9/16, 10/16, 15/16, 1/16, 3/16... z n = (z i h + z i k ) mod 2. (13) Lecture: Generating random numbers 22

23 3.8. Composite generator Idea: use two generators of low period to generate another with wider period. The basic principle: use the first generator to fill the shuffling table (address - entry (random number)); use random numbers of second generator as addresses in the next step; each number corresponding to the address is replaced by new random number of first generator. The following algorithm uses one generator to shuffle with itself: 1. create shuffling table of 100 entries (i, t i = γ i, i = 1, 2,..., 100); 2. draw random number γ k and normalize to the range (1, 100); 3. entry i of the table gives random number t i ; 4. draw the next random number γ k+1 and update t i = γ k+1 ; 5. repeat from step 2. Note: table with 100 entries gives fairly good results. Lecture: Generating random numbers 23

24 4. Tests for random number generators What do we want to check: independence; uniformity. Important notes: if and only if tests passed number can be treated as random; recall: numbers are actually deterministic! Commonly used tests for independence: runs test; correlation test. Commonly used tests for uniformity: Kolmogorov s test; χ 2 test. Lecture: Generating random numbers 24

25 4.1. Independence: runs test Basic idea: compute patterns of numbers (always increase, always decrease, etc.); compare to theoretical probabilities. 1/3 1/3 1/3 1/3 1/3 1/3 Figure 2: Illustration of the basic idea. Lecture: Generating random numbers 25

26 Do the following: consider a sequence of pseudo random numbers: {u i, i = 0, 1,..., n}; consider unbroken subsequences of numbers where numbers are monotonically increasing; such subsequence is called run-up; example: 0.78,081,0.89,0.81 is a run-up of length 3. compute all run-ups of length i: r i, i = 1, 2, 3, 4, 5; all run-ups of length i 6 are grouped into r 6. calculate: R = 1 n 1 i,j 6 (r i nb i )(r j nb j )a ij, 1 i, j 6, (14) where (b 1, b 2,..., b 6 ) = ( 1 6, 5 24, , 19 ) 720, , 1, (15) 840 Lecture: Generating random numbers 26

27 Coefficients a ij must be chosen as an element of the matrix: Statistics R has χ 2 distribution: number of freedoms: 6; n > If so, observations are i.i.d. Lecture: Generating random numbers 27

28 4.2. Independence: correlation test Basic idea: compute autocorrelation coefficient for lag-1; if it is not zero and this is statistically significant result, numbers are not independent. Compute statistics (lag-1 autocorrelation coefficient) as: R = N (u j E[u])(u j+1 E[u])/ j=1 N (u j E[j]) 2. (16) j=1 Practice: if R is relatively big there is serial correlation. Important notes: exact distribution of R is unknown; for large N: if u j uncorrelated we have: P r{ 2/ N R 2/ N}; therefore: reject hypotheses of non-correlated at 5% level if R is not in { 2/ N, 2/ N}. Notes: other tests for correlation Ljung and Box test, Portmanteau test, etc. Lecture: Generating random numbers 28

29 4.3. Uniformity: χ 2 test The algorithm: divide [0, 1] into k, k > 100 non-overlapping intervals; compute the relative frequencies of falling in each category, f i : ensure that there are enough numbers to get f i > 5, i = 1, 2,..., k; values f i > 5, i = 1, 2,..., k are called observed values. if observations are truly uniformly distributed then: these values should be equal to r i = n/k, i = 1, 2,..., k; these values are called theoretical values. compute χ 2 statistics for uniform distribution: χ 2 = k n that must have k 1 degrees of freedom. k i=1 ( f i n k ) 2. (17) Lecture: Generating random numbers 29

30 Hypotheses: H 0 observations are uniformly distributed; H 1 observations are not uniformly distributed. H 0 is rejected if: computed value of χ 2 is greater than one obtained from the tables; you should check the entry with k 1 degrees of freedom and 1-a level of significance. Lecture: Generating random numbers 30

31 4.4. Kolmogorov test Facts about this test: compares empirical distribution with theoretical ones; empirical: F N (x) number of smaller than or equal to x, divided by N; theoretical: uniform distribution in (0, 1): F (x) = x, 0 < x < 1. Hypotheses: H 0 : F N (x) follows F (x); H 1 : F N (x) does not follow F (x). Statistics: maximum absolute difference over a range: R = max F (x) F N (x). (18) if R > R α : H 0 is rejected; if R R α : H 0 is accepted. Note: use tables for N, α (significance level), to find R α. Lecture: Generating random numbers 31

32 Example: we got 0.44, 0.81, 0.14, 0.05, 0.93: H 0 : random numbers follows uniform distribution; we have to compute: R (j) j/n j/n R (j) R (j) (j-1)/n compute statistics as: R = max F (x) F N (x) = 0.26; from tables: for α = 0.05, R α = > R; H 0 is accepted, random numbers are distributed uniformly in (0, 1). Lecture: Generating random numbers 32

33 4.5. Other tests The serial test: consider pairs (u 1, u 2 ), (u 3, u 4 ),..., (u 2N 1, u 2N ); count how many observations fall into N 2 different subsquares of the unit square; apply χ 2 test to decide whether they follow uniform distribution; one can formulate M-dimensional version of this test. The permutation test look at k-tuples: (u 1, u k ), (u k+1, u 2k ),..., (u (N 1)k+1, u Nk ); in a k-tuple there k! possible orderings; in a k-tuple all orderings are equally likely; determine frequencies of orderings in k-tuples; apply χ 2 test to decide whether they follow uniform distribution. Lecture: Generating random numbers 33

34 The gap test let J be some fixed subinterval in (0, 1); if we have that: u n+j not in J, 0 j k, and both u n 1 J, u n+k+1 J; we say that there is a gap of length k. H 0 : numbers are independent and uniformly distributed in (0, 1): gap length must be geometrically distributed with some parameter p; p is the length of interval J: P r{gap of length k} = p(1 p) k. (19) practice: we observe a large number of gaps, say N; choose an integer and count number of gaps of length 0, 1,..., h 1 and h; apply χ 2 test to decide whether they independent and follow uniform distribution. Lecture: Generating random numbers 34

35 4.6. Important notes Some important notes on seed number: do not use seed 0; avoid even values; do not use the same sequence for different purposes in a single simulation run. Note: these instruction may not be applicable for a particular generator. General notes: some common generators are found to be inadequate; even if generator passed tests, some underlying pattern might still be undetected; if the task is important use composite generator. Lecture: Generating random numbers 35

36 5. Random numbers with arbitrary distribution Discrete distributions: discretization; for any discrete distribution. rescaling: for uniform random numbers in (a, b). methods for specific distributions. Continuous distributions: inverse transform; rejection method; composition method; methods for specific distributions. Lecture: Generating random numbers 36

37 5.1. Discrete distributions: discretization Consider arbitrary distributed discrete RV: P r{x = x j } = p j, j = 0, 1,..., p j = 1. (20) j=0 The following method can be applied: generate uniformly distributed RV; use the following to generate discrete RV: this method can be applied to any discrete RV; there are some specific methods for specific discrete RVs. Lecture: Generating random numbers 37

38 Figure 3: Illustration of the proposed approach. Lecture: Generating random numbers 38

39 The step-by-step procedure: compute probabilities p i = P r{x = x i }, i = 0, 1,... ; generate RV u with U(0, 1); if u < p 0, set X = x 0 ; if u < p 0 + p 1, set X = x 1 ; if u < p 0 + p 1 + p 2, set X = x 2 ;... Note the following: this is inverse transform method for discrete RVs: we determine the value of u; we determine the interval [F (x i 1 ), F (x i )] in which it lies. complexity depends on the number of intervals to be searched. Lecture: Generating random numbers 39

40 Example: p 1 = 0.2, p 2 = 0.1, p 3 = 0.25, p 4 = 0.45: determine generator for P r{x = x j } = p j. Algorithm 1: generate u = U(0, 1); if u < 0.2, set X = 1, return; if u < 0.3, set X = 2; if u < 0.55, set X = 3; set X = 4. Algorithm 2 (more effective): generate u = U(0, 1); If u < 0.45, set X = 4; if u < 0.7, set X = 3; if u < 0.9, set X = 1; set X = 2. Lecture: Generating random numbers 40

41 5.2. Example of discretization: Poisson RV Poisson RV have the following distribution: p i = P r{x = i} = λi e λ, i! i = 0, 1,.... (21) We use the property: p i+1 = λ i + 1 p i, i = 1, 2,.... (22) The algorithm: 1. generate u = U(0, 1); 2. i = 0, p = e λ, F = p; 3. if u < F, set X = i; 4. p = λp/(i + 1), F = F + p, i = i + 1; 5. go to step 3. Lecture: Generating random numbers 41

42 5.3. Example of discretization: binomial RV Binomial RV have the following distribution: p i = P r{x = i} = We are going to use the following property: The algorithm: 1. generate u = U(0, 1); p i+1 = n i i c = p/(1 p), i = 0, d = (1 p)n, F = d; 3. if u < F, set X = i 4. d = [c(n i)/(i + 1)]d, F = F + d, i = i + 1; 5. go to step 3. n! i!(n i)! pi (1 p) n i, i = 0, 1,.... (23) p 1 p p i, i = 0, 1,.... (24) Lecture: Generating random numbers 42

43 5.4. Continuous distributions: inverse transform method Inverse transform method: applicable only when cdf can be inversed analytically; works for a number of distributions: exponential, unform, Weibull, etc. Assume: we would like to generate numbers with pdf f(x) and cdf F (x); recall, F (x) is defined on [0, 1]. The generic algorithm: generate u = U(0, 1); set F (x) = u; find x = F 1 (u), F 1 ( ) is the inverse transformation of F ( ). Lecture: Generating random numbers 43

44 Example: we want to generate numbers from the following pdf f(x) = 2x, 0 x 1; calculate the cdf as follows: F (x) = x 0 2tdt = x 2, 0 x 1. (25) let u be the random number, we have u = x 2 or u = x; get the random number. Lecture: Generating random numbers 44

45 5.5. Inverse transform method: uniform continuous distribution Uniform continuous distribution has the following pdf and cdf: 1 (b a) f(x) =, a < x < b (x a) 0, otherwise, F (x) =, a < x < b (b a) 0, otherwise. (26) The algorithm: generate u = U(0, 1); set u = F (x) = (x a)/(b a); solve to get x = a + (b a)u. Lecture: Generating random numbers 45

46 5.6. Inverse transform method: exponential distribution Exponential distribution has the following pdf and cdf: f(x) = λe λx, F (x) = 1 e λx, λ > 0, x 0. (27) The algorithm: generate u = U(0, 1); set u = F (x) = e λx ; solve to get x = (1/λ) log u. Lecture: Generating random numbers 46

47 5.7. Inverse transform method: Erlang distribution Erlang distribution: convolution of k exponential distributions. The algorithm: generate u = U(0, 1); sum of exponential variables x 1,..., x k with mean 1/λ; solve to get: x = k x i = 1 λ i=1 k log u i = 1 k λ log u i. (28) i=1 i=1 Lecture: Generating random numbers 47

48 5.8. Specific methods: normal distribution Normal distribution has the following pdf: f(x) = 1 σ 1 2π e 2 (x µ) 2 where σ and µ are the standard deviation and the mean. σ 2, < x <, (29) Standard normal distribution (RV Z = (X µ/)σ) has the following pdf: f(z) = 1 2π e 1 2 z2, < z <, where µ = 0, σ = 1. (30) Lecture: Generating random numbers 48

49 Central limit theorem: if x 1, x 2,..., x n are independent with E[x i ] = µ, σ 2 [x i ] = σ 2, i = 1, 2,..., n; the sum of them approaches normal distribution if n : E[ x i ] = nµ, σ 2 [ x i ] = nσ 2. The approach: generate k random numbers u i = U(0, 1), i = 0, 1,..., k 1; each random numbers has: E[u i ] = (0 + 1)/2 = 1/2, σ 2 [u i ] = (1 0) 2 /12 = 1/12; sum of these number follows normal distribution with: ( ) k ui N 2, k ui k/2, or 12 k/ N(0, 1). (31) 12 if the RV we want to generate is x with mean µ and standard deviation σ: x µ σ finally (note that k should be at least 10): x µ ui k/2 12 = σ k/, or x = σ 12 k N(0, 1). (32) ( ui k ) + µ. (33) 2 Lecture: Generating random numbers 49

50 5.9. Specific method: empirical continuous distributions Assume we have a histogram: x i is the midpoint of the interval i; f(x i ) is the length of the ith rectangle. Note: the task is different from sampling from discrete distribution. Lecture: Generating random numbers 50

51 Construct the cdf as follows: F (x i ) = k {F (x i 1 ),F (x i )} f(x k ), (34) which is monotonically increasing within each interval [F (x i 1 ), F (x i )]. Lecture: Generating random numbers 51

52 The algorithm: generate u = U(0, 1); assume that u {F (x i 1 ), F (x i )}; use the following linear interpolation to get: u F (x i 1 ) x = x i 1 + (x i x i 1 ) F (x i ) F (x i 1 ). (35) Note: this approach can also be used for analytical continuous distribution. get (x i, f(x i )), i = 1, 2,..., k and follow the procedure. Lecture: Generating random numbers 52

53 5.10. Rejection method Works when: pdf f(x) is bounded; x has a finite range, say a x b. The basic steps are: normalize the range of f(x) by a scale factor such that cf(x) 1, a x b; define x as a linear function of u = U(0, 1), i.e. x = a + (b a)u; generate pairs of random numbers (u 1, u 2 ), u 1, u 2 = U(0, 1); accept the pair and use x = a + (b a)u 1 whenever: the pair satisfies u 2 cf(a + (b a)u 1 ); meaning that the pair (x, u 2 ) falls under the curve of cf(x). Lecture: Generating random numbers 53

54 The underlying idea: P r{u 2 cf(x)} = cf(x); if x is chosen at random from (a, b): we reject if u 2 > cf(x); we accept if u 2 cf(x); we match f(x). Lecture: Generating random numbers 54

55 Example: generate numbers from f(x) = 2x, 0 x 1: 1. select c such that cf(x) 1: for example: c = generate u 1 and set x = u 1 ; 3. generate u 2 : if u 2 < cf(u 1 ) = (0.5)2u 1 = u 1 then accept x; otherwise go back to step 2. Lecture: Generating random numbers 55

56 5.11. Convolution method The basis of the method is the representation of cdf F (x): F (x) = p j F j (x), (36) j=1 p j 0, j = 1, 2,..., j=1 p j = 1. Works when: it is easy to to generate RVs with distribution F j (x) than F (x); hyperexponential RV; Erlang RV. The algorithm: 1. generate discrete RV J, P r{j = j} = p j ; 2. given J = j generate RV with F j (x); 3. compute j=1 p jf j (x). Lecture: Generating random numbers 56

57 Example: generate from exponential distribution: divide (0, ) into intervals (i, i + 1), i = 0, 1,... ; the probabilities of intervals are given: p i = P r{i X < i + 1} = e i e (i+1) = e i (1 e 1 ), (37) gives geometric distribution. the conditional pdfs are fiven by: f i (x) = e (x i) /(1 e 1 ), i x < i + 1. (38) in the interval i(x i) has the pdf e x /(1 e 1 ), 0 x < 1. The algorithm: get I from geometric distribution p i = e i /(1 e 1 ), i = 0, 1,... ; get Y from e x /(1 e 1 ), 0 x < 1; X = I + Y. Lecture: Generating random numbers 57

58 6. Statistical tests for RNs with arbitrary distribution What we have to test for: independence; particular distribution. Tests for independence: correlation tests: Portmanteau test, modified Portmanteau test, ±2/ n, etc. note: here we test only for linear dependence... Tests for distribution: χ 2 test; Kolmogorov s test. Lecture: Generating random numbers 58

59 7. Multi-dimensional distributions Task: generate samples from RV (X 1, X 2,..., X n ). Write the joint density function as: f(x 1, x 2,..., x n ) = f 1 (x 1 )f 2 (x 2 x 1 )... f(x n x 1... x n 1 ). (39) f 1 (x 1 ) is the marginal distribution of X 1 ; f k (x k x 1,..., x k 1 ) is the conditional pdf of X k with condition on X 1 = x 1,..., X k 1 = x k 1. The basic idea: generate one number at a time: get x 1 from f 1 (x 1 ); get x 2 from f 2 (x 2 x 1 ), etc. The algorithm: get n random numbers u i = U(0, 1), i = 0, 1,..., n; subsequently get the following RVs: F 1 (X 1 ) = u 1, F 2 (X 2 X 1 ) = u 2,... F n (X n X 1,..., X n 1 ) = u n. (40) Lecture: Generating random numbers 59

60 Example: generate from f(x, y) = x + y: marginal pdf and cdf of X are given by: f(x) = 1 0 f(x, y)dy = x + 1 2, x F (x) = f(x )dx = 1 2 (x2 + x). (41) 0 conditional pdf and cdf of Y are given by: f(y x) = f(x, y) f(x) = x + y x + 1, F (y x) = 2 y 0 f(y x)dy = xy y2 x + 1. (42) 2 by inversion we get: x = 1 2 ( 8u ), y = x 2 + u 2 (1 + 2x) x. (43) Lecture: Generating random numbers 60