1. 1 Part 6 Markov Chains

2. Markov Chains (1)  A Markov chain is a mathematical model for stochastic systems whose states, discrete or continuous, are governed by transition probability.  Suppose the random variable take state space (Ω) that is a countable set of value. A Markov chain is a process that corresponds to the network. 2

3. Markov Chains (2)  The current state in Markov chain only depends on the most recent previous states. Transition probability where  http://en.wikipedia.org/wiki/Markov_chain http://civs.stat.ucla.edu/MCMC/MCMC_tuto rial/Lect1_MCMC_Intro.pdf http://en.wikipedia.org/wiki/Markov_chain http://civs.stat.ucla.edu/MCMC/MCMC_tuto rial/Lect1_MCMC_Intro.pdf 3

4. An Example of Markov Chains  where is initial state and so on. is transition matrix. 4 1 2 3 4 5

5. Definition (1)  Define the probability of going from state i to state j in n time steps as  A state j is accessible from state i if there are n time steps such that, where  A state i is said to communicate with state j (denote: ), if it is true that both i is accessible from j and that j is accessible from i. 5

6. Definition (2)  A state i has period if any return to state i must occur in multiples of time steps.  Formally, the period of a state is defined as  If, then the state is said to be aperiodic; otherwise ( ), the state is said to be periodic with period. 6

7. Definition (3)  A set of states C is a communicating class if every pair of states in C communicates with each other.  Every state in a communicating class must have the same period  Example: 7

8. Definition (4)  A finite Markov chain is said to be irreducible if its state space (Ω) is a communicating class; this means that, in an irreducible Markov chain, it is possible to get to any state from any state.  Example: 8

9. Definition (5)  A finite state irreducible Markov chain is said to be ergodic if its states are aperiodic  Example: 9

10. Definition (6)  A state i is said to be transient if, given that we start in state i, there is a non-zero probability that we will never return back to i.  Formally, let the random variable T i be the next return time to state i (the “hitting time”):  Then, state i is transient iff there exists a finite T i such that: 10

11. Definition (7)  A state i is said to be recurrent or persistent iff there exists a finite T i such that:.  The mean recurrent time.  State i is positive recurrent if is finite; otherwise, state i is null recurrent.  A state i is said to be ergodic if it is aperiodic and positive recurrent. If all states in a Markov chain are ergodic, then the chain is said to be ergodic. 11

12. Stationary Distributions  Theorem: If a Markov Chain is irreducible and aperiodic, then  Theorem: If a Markov chain is irreducible and aperiodic, then and where is stationary distribution. 12

13. Definition (8)  A Markov chain is said to be reversible, if there is a stationary distribution such that  Theorem: if a Markov chain is reversible, then 13

14. An Example of Stationary Distributions  A Markov chain:  The stationary distribution is 14 2 13 0.4 0.3 0.7 0.3

15. Properties of Stationary Distributions  Regardless of the starting point, the process of irreducible and aperiodic Markov chains will converge to a stationary distribution.  The rate of converge depends on properties of the transition probability. 15

16. 16 Part 7 Monte Carlo Markov Chains

17. Applications of MCMC  Simulation: Ex: where are known.  Integration: computing in high dimensions. Ex:  Bayesian Inference: Ex: Posterior distributions, posterior means… 17

18. Monte Carlo Markov Chains  MCMC method are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its stationary distribution.  The state of the chain after a large number of steps is then used as a sample from the desired distribution.  http://en.wikipedia.org/wiki/MCMC http://en.wikipedia.org/wiki/MCMC 18

19. Inversion Method vs. MCMC (1)  Inverse transform sampling, also known as the probability integral transform, is a method of sampling a number at random from any probability distribution given its cumulative distribution function (cdf).  http://en.wikipedia.org/wiki/Inverse_transf orm_sampling_method http://en.wikipedia.org/wiki/Inverse_transf orm_sampling_method 19

20. Inversion Method vs. MCMC (2)  A random variable with a cdf, then has a uniform distribution on [0, 1].  The inverse transform sampling method works as follows: 1. Generate a random number from the standard uniform distribution; call this. 2. Compute the value such that ; call this. 3. Take to be the random number drawn from the distribution described by. 20

21. Inversion Method vs. MCMC (3)  For one dimension random variable, Inversion method is good, but for two or more high dimension random variables, Inverse Method maybe not.  For two or more high dimension random variables, the marginal distributions for those random variables respectively sometime be calculated difficult with more time. 21

22. Gibbs Sampling  One kind of the MCMC methods.  The point of Gibbs sampling is that given a multivariate distribution it is simpler to sample from a conditional distribution rather than integrating over a joint distribution.  George Casella and Edward I. George. "Explaining the Gibbs sampler". The American Statistician, 46:167-174, 1992. (Basic summary and many references.)  http://en.wikipedia.org/wiki/Gibbs_samplin g http://en.wikipedia.org/wiki/Gibbs_samplin g 22

23. Example 1 (1)  To sample x from: where are known is a constant  One can see that 23

24. Example 1 (2)  Gibbs sampling Algorithm: Initial Setting: or a arbitrary value For, sample a value from Return 24

25. Example 1 (3)  Under regular conditions:  How many steps are needed for convergence?  Within an acceptable error, such as  is large enough, such as. 25

26. Example 1 (4)  Inversion Method: is Beta-Binomial distribution. The cdf of is that has a uniform distribution on [0, 1]. 26

27. Gibbs sampling by R (1) N = 1000; num = 16; alpha = 5; beta = 7 tempy <- runif(1); tempx <- rbeta(1, alpha, beta) j = 0; Forward = 1; Afterward = 0 while((abs(Forward-Afterward) > 0.0001) && (j <= 1000)){ Forward = Afterward; Afterward = 0 for(i in 1:N){ tempy <- rbeta(1, tempx+alpha, num-tempx+beta) tempx <- rbinom(1, num, tempy) Afterward = Afterward+tempx } Afterward = Afterward/N; j = j+1 } sample <- matrix(0, nrow = N, ncol = 2) for(i in 1:N){ tempy <- rbeta(1, tempx+alpha, num-tempx+beta) tempx <- rbinom(1, num, tempy) 27

28. Gibbs sampling by R (2) sample[i, 1] = tempx; sample[i, 2] = tempy } sample_Inverse <- rbetabin(N, num, alpha, beta) write(t(sample), "Sample for Ex1 by R.txt", ncol = 2) Xhist <- cbind(hist(sample[, 1], nclass = num)$count, hist(sample_Inverse, nclass = num)$count) write(t(Xhist), "Histogram for Ex1 by R.txt", ncol = 2) prob <- matrix(0, nrow = num+1, ncol = 2) for(i in 0:num){ if(i == 0){ prob[i+1, 2] = mean(pbinom(i, num, sample[, 2])) prob[i+1, 1] = gamma(alpha+beta)*gamma(num+beta) prob[i+1, 1] = prob[i+1, 1]/(gamma(beta)*gamma(num+beta+alpha)) } else{ 28 Inverse method

29. Gibbs sampling by R (3) if(i == 1){ prob[i+1, 1] = num*alpha/(num-1+alpha+beta) for(j in 0:(num-2)) prob[i+1, 1] = prob[i+1, 1]*(beta+j)/(alpha+beta+j) } else prob[i+1, 1] = prob[i+1, 1]*(num-i+1)/(i)*(i- 1+alpha)/(num-i+beta) prob[i+1, 2] = mean((pbinom(i, num, sample[, 2])- pbinom(i-1, num, sample[, 2]))) } if(i != num) prob[i+2, 1] = prob[i+1, 1] } write(t(prob), "ProbHistogram for Ex1 by R.txt", ncol = 2) 29

30. Inversion Method by R (1) rbetabin <- function(N, size, alpha, beta){ Usample <- runif(N) Pr_0 = gamma(alpha+beta)*gamma(size+beta)/gamma(beta)/gam ma(size+beta+alpha) Pr = size*alpha/(size-1+alpha+beta) for(i in 0:(size-2)) Pr = Pr*(beta+i)/(alpha+beta+i) Pr_Initial = Pr sample <- array(0,N) CDF <- array(0, (size+1)) CDF[1] <- Pr_0 30

31. Inversion Method by R (2) for(i in 1:size){ CDF[i+1] = CDF[i]+Pr Pr = Pr*(size-i)/(i+1)*(i+alpha)/(size-i-1+beta) } for(i in 1:N){ sample[i] = which.min(abs(Usample[i]-CDF))-1 } return(sample) } 31

32. Gibbs sampling by C/C++ (1) 32

33. Gibbs sampling by C/C++ (2) 33 Inverse method

34. Gibbs sampling by C/C++ (3) 34

35. Gibbs sampling by C/C++ (4) 35

36. Inversion Method by C/C++ (1) 36

37. Inversion Method by C/C++ (2) 37

38. Plot Histograms by Maple (1)  Figure 1:1000 samples with n=16, α=5 and β=7. 38 Blue-Inversion method Red-Gibbs sampling

39. Plot Histograms by Maple (2) 39

40. Probability Histograms by Maple (1)  Figure 2: Blue histogram and yellow line are pmf of x. Red histogram is from Gibbs sampling. 40

41. Probability Histograms by Maple (2)  The probability histogram of blue histogram of Figure 1 would be similar to the bule probability histogram of Figue 2, when the sample size.  The probability histogram of red histogram of Figure 1 would be similar to the red probability histogram of Figue 2, when the iteration n. 41

42. Probability Histograms by Maple (3) 42

43. Exercises  Write your own programs similar to those examples presented in this talk, including Example 1 in Genetics and other examples.  Write programs for those examples mentioned at the reference web pages.  Write programs for the other examples that you know. 43

44. 44 Bayesian Methods with Monte Carlo Markov Chains III Henry Horng-Shing Lu Institute of Statistics National Chiao Tung University hslu@stat.nctu.edu.tw http://tigpbp.iis.sinica.edu.tw/courses.htm

45. 45 Part 10 More Examples of Gibbs Sampling

46. An Example with Three Random Variables (1)  To sample as follows: where is known, is a constant. 46

47. An Example with Three Random Variables (2)  One can see that 47

48.  Gibbs sampling Algorithm: 1. Initial Setting:, or an arbitrary value or an arbitrary integer 2. Sample a value from 3., repeat step 2 until convergence. 48 An Example with Three Random Variables (3)

49. An Example with Three Random Variables by R N = 10000; alpha = 2; beta = 4; lambda = 16 sample <- matrix(0, nrow = N, ncol = 3) tempY <- runif(1); tempN <- 1 tempX <- rbinom(1, tempN, tempY) j = 0; forward = 1; afterward = 0 while((abs(forward-afterward) > 0.001) && (j <= 1000)){ forward = afterward; afterward = 0 for(i in 1:N){ tempY <- rbeta(1, tempX+alpha, tempN-tempX+beta) tempN <- rpois(1, (1-tempY)*lambda) tempN = tempN+tempX tempX <- rbinom(1, tempN, tempY) afterward = afterward+tempX } afterward = afterward/N; j = j+1 } 49 10000 samples with α=2, β=4 and λ=16

50. for(i in i:N){ tempY <- rbeta(1, tempX+alpha, tempN-tempX+beta) tempN <- rpois(1, (1-tempY)*lambda) tempN = tempN+tempX tempX <- rbinom(1, tempN, tempY) sample[i, 2] = tempY sample[i, 3] = tempN sample[i, 1] = tempX } 50 An Example with Three Random Variables by R

51. An Example with 3 Random Variables by C (1) 51 10000 samples with α=2, β=4 and λ=16

52. 52 An Example with 3 Random Variables by C (2)

53. Example 1 in Genetics (1)  Two linked loci with alleles A and a, and B and b A, B: dominant a, b: recessive  A double heterozygote AaBb will produce gametes of four types: AB, Ab, aB, ab 53 A Bb a B A b a 1/2 a B b A A B b a

54. Example 1 in Genetics (2)  Probabilities for genotypes in gametes 54 No RecombinationRecombination Male1-rr Female1-r ’ r’r’ ABabaBAb Male(1-r)/2 r/2 Female(1-r ’ )/2 r ’ /2 A Bb a B A b a 1/2 a B b A A B b a

55. Example 1 in Genetics (3)  Fisher, R. A. and Balmukand, B. (1928). The estimation of linkage from the offspring of selfed heterozygotes. Journal of Genetics, 20, 79–92.  More: http://en.wikipedia.org/wiki/Genetics http://www2.isye.gatech.edu/~brani/isyeba yes/bank/handout12.pdf 55

56. Example 1 in Genetics (4) 56 MALE AB (1-r)/2 ab (1-r)/2 aB r/2 Ab r/2 FEMALEFEMALE AB (1-r ’ )/2 AABB (1-r) (1-r ’ )/4 aABb (1-r) (1-r ’ )/4 aABB r (1-r ’ )/4 AABb r (1-r ’ )/4 ab (1-r ’ )/2 AaBb (1-r) (1-r ’ )/4 aabb (1-r) (1-r ’ )/4 aaBb r (1-r ’ )/4 Aabb r (1-r ’ )/4 aB r ’ /2 AaBB (1-r) r ’ /4 aabB (1-r) r ’ /4 aaBB r r ’ /4 AabB r r ’ /4 Ab r ’ /2 AABb (1-r) r ’ /4 aAbb (1-r) r ’ /4 aABb r r ’ /4 AAbb r r ’ /4

57. Example 1 in Genetics (5)  Four distinct phenotypes: A*B*, A*b*, a*B* and a*b*.  A*: the dominant phenotype from (Aa, AA, aA).  a*: the recessive phenotype from aa.  B*: the dominant phenotype from (Bb, BB, bB).  b*: the recessive phenotype from bb.  A*B*: 9 gametic combinations.  A*b*: 3 gametic combinations.  a*B*: 3 gametic combinations.  a*b*: 1 gametic combination.  Total: 16 combinations. 57

58. Example 1 in Genetics (6)  Let, then 58

59. Example 1 in Genetics (7)  Hence, the random sample of n from the offspring of selfed heterozygotes will follow a multinomial distribution: We know that and So 59

60. Example 1 in Genetics (8)  Suppose that we observe the data of which is a random sample from Then the probability mass function is 60

61. Example 1 in Genetics (9)  How to estimate ? MME (shown in the last week): http://en.wikipedia.org/wiki/Method_of_moment s_%28statistics%29 http://en.wikipedia.org/wiki/Method_of_moment s_%28statistics%29 MLE (shown in the last week): http://en.wikipedia.org/wiki/Maximum_likelihoo d http://en.wikipedia.org/wiki/Maximum_likelihoo d Bayesian Method: http://en.wikipedia.org/wiki/Bayesian_method 61

62. Example 1 in Genetics (10)  As the value of is between ¼ and 1, we can assume that the prior distribution of is.  The posterior distribution is  The integration in the above denominator, does not have a closed form. 62

63. Example 1 in Genetics (11)  We will consider the mean of posterior distribution (the posterior mean),  The Monte Carlo Markov Chains method is a good method to estimate even if and the posterior mean do not have closed forms. 63

64. Example 1 by R  Direct numerical integration when : > y <- c(125, 18, 20, 24) > phi <- runif(1000000, 1/4, 1) > f_phi <- function(phi){((2+phi)/4)^y[1]*((1- phi)/4)^(y[2]+y[3])*(phi/4)^y[4]} > mean(f_phi(phi)*phi)/mean(f_phi(phi)) [1] 0.573808  We can assume other prior distributions to compare the results of posterior means:,,,,, 64

65. Example 1 by C/C++ 65 Replace other prior distribution, such as Beta(1,1), …,Beta(1e-5,1e-5)

66. Beta Prior 66

67. 67 Estimate Method MME0.683616Bayesian Beta(2,3) 0.564731 MLE0.663165Bayesian Beta(3,2) 0.577575 Bayesian U( ¼,1) 0.573931Bayesian Beta( ½, ½ ) 0.574928 Bayesian Beta(1,1) 0.573918Bayesian Beta(10 -5,10 -5 ) 0.588925 Bayesian Beta(2,2) 0.572103Bayesian Beta(10 -7,10 -7 ) show below Comparison for Example 1 (1)

68. 68 Estimate Method Bayesian Beta(10,10) 0.559905Bayesian Beta(10 -7,10 -7 ) 0.193891 Bayesian Beta(10 2,10 2 ) 0.520366Bayesian Beta(10 -7,10 -7 ) 0.400567 Bayesian Beta(10 4,10 4 ) 0.500273Bayesian Beta(10 -7,10 -7 ) 0.737646 Bayesian Beta(10 5,10 5 ) 0.500027Bayesian Beta(10 -7,10 -7 ) 0.641388 Bayesian Beta(10 n,10 n ) Bayesian Beta(10 -7,10 -7 ) Not stationary Comparison for Example 1 (2)

69. 69 Part 11 Gibbs Sampling Strategy

70. Sampling Strategy (1)  Strategy I: Run one chain for a long time. After some “Burn-in” period, sample points every some fixed number of steps. The code example of Gibbs sampling in the previous lecture use sampling strategy I.  http://www.cs.technion.ac.il/~cs236372/tir gul09.ps http://www.cs.technion.ac.il/~cs236372/tir gul09.ps 70 Burn-inN samples from one chain

71. Sampling Strategy (2)  Strategy II: Run the chain N times, each run for M steps. Each run starts from a different state points. Return the last state in each run. 71 N samples from the last sample of each chain Burn-in

72. Sampling Strategy (3)  Strategy II by R: N = 100; num = 16; alpha = 5; beta = 7 sample <- matrix(0, nrow = N, ncol = 2) for(k in 1:N){ tempy <- runif(1); tempx <- rbeta(1, alpha, beta) j = 0; Forward = 1; Afterward = 0 while((abs(Forward-Afterward) > 0.001) && (j <= 100)){ Forward = Afterward; Afterward = 0 for(i in 1:N){ tempy <- rbeta(1, tempx+alpha, num- tempx+beta) tempx <- rbinom(1, num, tempy) Afterward = Afterward+tempx } Afterward = Afterward/N; j = j+1 } tempy <- rbeta(1, tempx+alpha, num-tempx+beta) tempx <- rbinom(1, num, tempy) sample[k, 1] = tempx; sample[k, 2] = tempy } 72

73. Sampling Strategy (4)  Strategy II by C/C++: 73

74. Strategy Comparison  Strategy I: Perform “burn-in” only once and save time. Samples might be correlated (--although only weakly).  Strategy II: Better chance of “covering” the space points especially if the chain is slow to reach stationary. This must perform “burn-in” steps for each chain and spend more time. 74

75. Hybrid Strategies (1)  Run several chains and sample few samples from each.  Combines benefits of both strategies. 75 N samples from each chain Burn-in

76. Hybrid Strategies (2)  Hybrid Strategy by R: tempN <- N; loc <- 1 sample <- matrix(0, nrow = N, ncol = 2) while(loc != (N+1)){ tempy <- runif(1); tempx <- rbeta(1, alpha, beta); j = 0 pN <- floor(runif(1)*(N-loc))+1 cat(pN, '\n‘); Forward = 1; Afterward = 0 while((abs(Forward-Afterward) > 0.001) && (j <= 100)){ Forward = Afterward; Afterward = 0 for(i in 1:N){ tempy <- rbeta(1, tempx+alpha, num-tempx+beta) tempx <- rbinom(1, num, tempy) Afterward = Afterward+tempx} Afterward = Afterward/N; j = j+1 } for(i in loc:(loc+pN-1)){ tempy <- rbeta(1, tempx+alpha, num-tempx+beta) tempx <- rbinom(1, num, tempy) sample[i, 1] <- tempx; sample[i, 2] <- tempy } loc <- i+1 } 76

77. Hybrid Strategies (3)  Hybrid Strategy by C/C++: 77

78. 78 Part 12 Metropolis-Hastings Algorithm

79. Metropolis-Hastings Algorithm (1)  Another kind of the MCMC methods.  The Metropolis-Hastings algorithm can draw samples from any probability distribution, requiring only that a function proportional to the density can be calculated at.  Process in three steps: Set up a Markov chain; Run the chain until stationary; Estimate with Monte Carlo methods. http://en.wikipedia.org/wiki/Metropolis- Hastings_algorithm http://en.wikipedia.org/wiki/Metropolis- Hastings_algorithm 79

80. Metropolis-Hastings Algorithm (2)  Let be a probability density (or mass) function (pdf or pmf).  is any function and we want to estimate  Construct the transition matrix of an irreducible Markov chain with states, where and is its unique stationary distribution. 80

81. Metropolis-Hastings Algorithm (3)  Run this Markov chain for times and calculate the Monte Carlo sum then  Sheldon M. Ross(1997). Proposition 4.3. Introduction to Probability Model. 7 th ed.  http://nlp.stanford.edu/local/talks/mcmc_2 004_07_01.ppt http://nlp.stanford.edu/local/talks/mcmc_2 004_07_01.ppt 81

82. Metropolis-Hastings Algorithm (4)  In order to perform this method for a given distribution, we must construct a Markov chain transition matrix with as its stationary distribution, i.e..  Consider the matrix was made to satisfy the reversibility condition that for all and.  The property ensures that for all and hence is a stationary distribution for 82

83. Metropolis-Hastings Algorithm (5)  Let a proposal be irreducible where, and range of is equal to range of.  But is not have to a stationary distribution of.  Process: Tweak to yield. 83 States from Q ij not π Tweak States from P ij π

84. Metropolis-Hastings Algorithm (6)  We assume that has the form where is called accepted probability, i.e. given, take 84

85. Metropolis-Hastings Algorithm (7)  For  WLOG for some,.  In order to achieve equality (*), one can introduce a probability on the left- hand side and set on the right- hand side. 85

86. Metropolis-Hastings Algorithm (8)  Then  These arguments imply that the accepted probability must be 86

87. Metropolis-Hastings Algorithm (9)  M-H Algorithm: Step 1: Choose an irreducible Markov chain transition matrix with transition probability. Step 2: Let and initialize from states in. Step 3 (Proposal Step): Given, sample form. 87

88. Metropolis-Hastings Algorithm (10)  M-H Algorithm (cont.): Step 4 (Acceptance Step): Generate a random number from If, set else Step 5:, repeat Step 3~5 until convergence. 88

89. 89  An Example of Step 3~5: Q ij X 1 = Y 1 X 2 = Y 1 X 3 = Y 3 ‧ X N P ij Tweak Y1Y2Y3‧‧‧YNY1Y2Y3‧‧‧YN X(t) Y Metropolis-Hastings Algorithm (11)

90. Metropolis-Hastings Algorithm (12)  We may define a “rejection rate” as the proportion of times t for which. Clearly, in choosing, high rejection rates are to be avoided.  Example: 90 XtXt π Y

91. Example (1)  Simulate a bivariate normal distribution: 91

92. Example (2)  Metropolis-Hastings Algorithm: 1. 2. Generate and that and are independent, then 3. 4. 5., repeat step 2~4 until convergence. 92

93. Example of M-H Algorithm by R (1) Pi <- function(x, mu, sigma){exp(-0.5*((x- mu)%*%chol2inv(sigma)%*%as.matrix(x- mu)))/(2*pi*sqrt(det(sigma)))} N <- 1000; mu <- c(3, 7) sigma <- matrix(c(1, 0.4, 0.4, 1), nrow = 2) sample <- matrix(0, nrow = N, ncol = 2) j = 0; tempX <- mu while((j 0.001)){ for(i in 1:N){ tempU <- c(runif(1, -1, 1), runif(1, -1, 1)) tempY <- tempX+tempU if(min(c(Pi(tempY, mu, sigma)/Pi(tempX, mu, sigma), 1)) > runif(1)){ tempX <- tempY; sample[i, ] <- tempY } 93

94. Example of M-H Algorithm by R (2) else{ tempX <- tempX; sample[i, ] <- tempX } j = j+1 } for(i in 1:N){ tempU <- c(runif(1, -1, 1), runif(1, -1, 1)) tempY <- tempX+tempU if(min(c(Pi(tempY, mu, sigma)/Pi(tempX, mu, sigma), 1)) > runif(1)){ tempX <- tempY; sample[i, ] <- tempY} else{ tempX <- tempX; sample[i, ] <- tempX } 94

95. Example of M-H Algorithm by C (1) 95

96. Example of M-H Algorithm by C (2) 96

97. Example of M-H Algorithm by C (3) 97

98. An Figure to Check Simulation Results  Black points are simulated samples; color points are probability density. 98 plot(sample, xlab = "X1", ylab = "X2") j = 0 for(i in seq(0.01, 0.3, 0.02)){ for(x in seq(0,6, 0.1)){ for(y in seq(4, 11, 0.1)){ if(abs(Pi(c(x, y), mu, sigma)-i) < 0.003) points(x, y, col = ((j)%2+2), pch = 19) } j = j+1 }

99. Exercises  Write your own programs similar to those examples presented in this talk, including Example 1 in Genetics and other examples.  Write programs for those examples mentioned at the reference web pages.  Write programs for the other examples that you know. 99