1. 1 Lesson 2: RNGs and Choosing from distributions Random number generators (RNG) Random number generators (RNG) General need for random numbers General need for random numbers Pseudo-random vs. random Pseudo-random vs. random Linear congruential generators Linear congruential generators Extension of period of generator by combining several generators Extension of period of generator by combining several generators Choosing from distributions Choosing from distributions Discrete Discrete Continuous: Direct Continuous: Direct Continuous: Rejection Continuous: Rejection Probability mixing Probability mixing Multidimensional distributions Multidimensional distributions Coding event problems: FORTRAN code MCBASE.FOR Coding event problems: FORTRAN code MCBASE.FOR

2. 2 Basic view of MC process We begin with the same black box drawing: We begin with the same black box drawing: Lesson 1 was concerned with the outputs. Lesson 1 was concerned with the outputs. Lesson 2 is concerned with the inputs. Lesson 2 is concerned with the inputs. The quality of outputs depends on quality of inputs. The quality of outputs depends on quality of inputs. The rest of the course is about the black box The rest of the course is about the black box

3. 3 The quest for randomness In the early days (1950s) of computer-based Monte Carlo, there was work done on MECHANICAL or ELECTRONIC random number generators to give us “true” binary randomness (0 or 1)=digits of fraction In the early days (1950s) of computer-based Monte Carlo, there was work done on MECHANICAL or ELECTRONIC random number generators to give us “true” binary randomness (0 or 1)=digits of fraction Relay flops Relay flops Decay rates (odd or even) in Decay rates (odd or even) in Etc. Etc. Many of them “filtered” the answer to correct for non-50/50 defects Many of them “filtered” the answer to correct for non-50/50 defects Two successive identical bits = 1 OR two successive different bits = 0 Two successive identical bits = 1 OR two successive different bits = 0 Closer to 50/50 but (at least) twice as slow Closer to 50/50 but (at least) twice as slow Abandoned as too slow, too unwieldy, and— ultimately—undesirable Abandoned as too slow, too unwieldy, and— ultimately—undesirable

4. 4 Pseudorandom generators (RNG) We cannot have (nor do we really want) truly random variables. We cannot have (nor do we really want) truly random variables. Any random number generator from a computer is not truly random (since it is repeatable). Any random number generator from a computer is not truly random (since it is repeatable). Referred to as “pseudorandom” Referred to as “pseudorandom” This repeatability is actually very important to us for validation reasons. Also, this repeatability is the basis of some Monte Carlo perturbation methods. This repeatability is actually very important to us for validation reasons. Also, this repeatability is the basis of some Monte Carlo perturbation methods. The most common form is that of "linear congruential generators" which generate a series of bounded integers, with each one using the one before it with the formula: The most common form is that of "linear congruential generators" which generate a series of bounded integers, with each one using the one before it with the formula:

5. 5 Possible difficulties Periodicity: Each number depends uniquely on the one before it. So once a number repeats, the rest of the series will be the same as it was before. How many unique numbers you get is the PERIOD. Periodicity: Each number depends uniquely on the one before it. So once a number repeats, the rest of the series will be the same as it was before. How many unique numbers you get is the PERIOD. It can be shown that a "full" period of m can be assured by choosing: It can be shown that a "full" period of m can be assured by choosing: a(mod 4)=1 a(mod 4)=1 m as a power of 2 m as a power of 2 b odd. b odd. The finiteness of the string also means that certain domains of the problem may be unreachable. (That is, there will be “gaps” of width 1/m.) [STARS] The finiteness of the string also means that certain domains of the problem may be unreachable. (That is, there will be “gaps” of width 1/m.) [STARS] We have to be careful not to depend on the later digits of a uniform deviate: they may be less random than the early digits. (They may be the “remnants” of repeating digits of fractions.) We have to be careful not to depend on the later digits of a uniform deviate: they may be less random than the early digits. (They may be the “remnants” of repeating digits of fractions.)

6. 6 Example: m=8, a=5, b=3, i0=1 #iai+b mod m  01800 1033.375 23182.25 32135.625 45284.5 54237.875 67386.75 76331.125

7. 7 Extending the period Even with “full period” L.C.G. random number generators, we still have the related problems: Even with “full period” L.C.G. random number generators, we still have the related problems: The period can be no longer than m The period can be no longer than m m must be a number that can be represented as an integer: m must be a number that can be represented as an integer: where N is the number of bits used to store integers (usu. 16. 32, or 64) Fortunately, we can solve this problem by combining the output of several uncorrelated L.C.G.’s: Fortunately, we can solve this problem by combining the output of several uncorrelated L.C.G.’s: The resulting period is the PRODUCT of the individual periods The resulting period is the PRODUCT of the individual periods The following slide shows how KENO and MCNP implement this The following slide shows how KENO and MCNP implement this

8. 8 KENO RNG (FORTRAN 77) DOUBLE PRECISION FUNCTION FLTRN() INTEGER X, Y, Z, X0, Y0, Z0, XM, YM, ZM DOUBLE PRECISION V, X1, Y1, Z1 EXTERNAL RANDNUM COMMON /QRNDOM/ X, Y, Z SAVE /QRNDOM/ PARAMETER ( MX = 157, MY = 146, MZ = 142 ) PARAMETER ( X0 = 32363, Y0 = 31727, Z0 = 31657 ) PARAMETER ( X1 = X0, Y1 = Y0, Z1 = Z0 ) X = MOD(MX*X,X0) Y = MOD(MY*Y,Y0) Z = MOD(MZ*Z,Z0) V = X/X1 + Y/Y1 + Z/Z1 FLTRN = V - INT(V) RETURNEND

9. 9 MCNP5 RNG (FORTRAN 95) integer, private, parameter :: R8 = selected_real_kind( 15, 307 ) integer, private, parameter :: R8 = selected_real_kind( 15, 307 ) integer, private, parameter :: I8 = selected_int_kind( 18 ) integer, private, parameter :: I8 = selected_int_kind( 18 )... integer(I8), private, SAVE :: & integer(I8), private, SAVE :: & & RN_MULT = 5_I8**19, & & RN_MULT = 5_I8**19, & & RN_ADD = 0_I8, & & RN_ADD = 0_I8, & & RN_STRIDE = 152917, & & RN_STRIDE = 152917, & & RN_SEED0 = 5_I8**19, & & RN_SEED0 = 5_I8**19, & & RN_MOD = 2_I8**48, & & RN_MOD = 2_I8**48, & & RN_MASK = 2_I8**48 - 1_I8, & & RN_MASK = 2_I8**48 - 1_I8, & & RN_PERIOD = 2_I8**46 & RN_PERIOD = 2_I8**46 real(R8), private, SAVE :: & real(R8), private, SAVE :: & & RN_NORM = 1.0_R8/2._R8**48 & RN_NORM = 1.0_R8/2._R8**48... function rang() function rang() implicit none implicit none real(R8) :: rang real(R8) :: rang RN_SEED = iand( iand( RN_MULT*RN_SEED, RN_MASK) + RN_ADD, RN_MASK) RN_SEED = iand( iand( RN_MULT*RN_SEED, RN_MASK) + RN_ADD, RN_MASK) rang = RN_SEED * RN_NORM rang = RN_SEED * RN_NORM RN_COUNT = RN_COUNT + 1 RN_COUNT = RN_COUNT + 1 return return end function rang end function rang

10. 10 “Dimensionality” Monte Carlo theory uses the word “dimension” in a way that is a little foreign to normal usage, so be ready Monte Carlo theory uses the word “dimension” in a way that is a little foreign to normal usage, so be ready The dimension of a Monte Carlo algorithm is how many random numbers have to be drawn to generate one estimate The dimension of a Monte Carlo algorithm is how many random numbers have to be drawn to generate one estimate Examples: Examples: Coin toss problem: 1 dimension Coin toss problem: 1 dimension Our pi problem: 2 dimension Our pi problem: 2 dimension Homework #1 problem of x1+x2>1.4: 2 dimension Homework #1 problem of x1+x2>1.4: 2 dimension Neutron transport problem simulation: ? Neutron transport problem simulation: ? Related to the “unit hypercube” point of view Related to the “unit hypercube” point of view

11. 11 “Discrepancy” Monte Carlo theory also has the special word “discrepancy” which is important Monte Carlo theory also has the special word “discrepancy” which is important The discrepancy of a RNG is a measure of the biggest “hole” in a ordered series of random numbers The discrepancy of a RNG is a measure of the biggest “hole” in a ordered series of random numbers Usually defined (the so-called “star discrepancy”) as the biggest difference between what fraction SHOULD have been chosen in a space from the origin (0,0,…,0) to any point (x,y,z,…) and the actual number that was ACTUALLY chosen [blackboard example] Usually defined (the so-called “star discrepancy”) as the biggest difference between what fraction SHOULD have been chosen in a space from the origin (0,0,…,0) to any point (x,y,z,…) and the actual number that was ACTUALLY chosen [blackboard example] The fraction that should have fallen in is x*y*z… The fraction that should have fallen in is x*y*z… You only have to check at actual chosen points (with and without the point) You only have to check at actual chosen points (with and without the point) To my surprise, the size of the discrepancy is proportional to To my surprise, the size of the discrepancy is proportional to It is the reason for the slow convergence of Monte Carlo It is the reason for the slow convergence of Monte Carlo

12. 12 “Discrepancy”

13. 13 Quasi-random numbers The logical extension of the idea of stratified sampling is to try to make N=M The logical extension of the idea of stratified sampling is to try to make N=M Each of the i=1,2,…,N random numbers is located in its own subdomain ((i-1)/N,i/N) Each of the i=1,2,…,N random numbers is located in its own subdomain ((i-1)/N,i/N) This looks (and acts) in 1D like a simple Riemann choice This looks (and acts) in 1D like a simple Riemann choice The trick is to make them jump around “randomly” The trick is to make them jump around “randomly” Result: Quasi-random numbers (also called “Low discrepancy sequences”) Result: Quasi-random numbers (also called “Low discrepancy sequences”) They are not really random, so they have to be used very carefully. They are not really random, so they have to be used very carefully. They are not used very much for Monte Carlo methods in radiation transport. They are not used very much for Monte Carlo methods in radiation transport. But, because they are of such importance to the general field of Monte Carlo (i.e., beyond transport methods), you should have a taste of what they are, how to get and use them. But, because they are of such importance to the general field of Monte Carlo (i.e., beyond transport methods), you should have a taste of what they are, how to get and use them.

14. 14 Quasi-random numbers (2) "Quasi"-random numbers are not random at all; they are a deterministic, equal-division, sequence that are "ordered" in such a way that they can be used by Monte Carlo methods. "Quasi"-random numbers are not random at all; they are a deterministic, equal-division, sequence that are "ordered" in such a way that they can be used by Monte Carlo methods. The easiest to generate are “van der Corput” sequences, which are found by "reflecting" the digits of the base counting integers about their radix point. The easiest to generate are “van der Corput” sequences, which are found by "reflecting" the digits of the base counting integers about their radix point. You pick a base. You pick a base. Count in that base. Count in that base. Reflect the number Reflect the number Interpret as base 10. Interpret as base 10.

15. 15 Quasi-random numbers (3) After M=Base N -1 numbers in the sequence, the sequence consists of the equally spaced fractions (i/(M+1), i=1,2,3,...M). After M=Base N -1 numbers in the sequence, the sequence consists of the equally spaced fractions (i/(M+1), i=1,2,3,...M). Therefore, the sequence is actually deterministic, not stochastic. (Like a numerical method.) Therefore, the sequence is actually deterministic, not stochastic. (Like a numerical method.) The beauty of the sequence is that the re-ordering results in the an (almost) even coverage of the domain (0,1) even if you stop in the middle. The beauty of the sequence is that the re-ordering results in the an (almost) even coverage of the domain (0,1) even if you stop in the middle. Because you need ALL of the numbers in the sequence to cover the (0,1) range (which is not true of the pseudo-random sequence), it is important that all of the numbers of the sequence be used on the SAME decision. Because you need ALL of the numbers in the sequence to cover the (0,1) range (which is not true of the pseudo-random sequence), it is important that all of the numbers of the sequence be used on the SAME decision. So, if your problem has more than one decision (e.g., the pi problem had 2—x and y coordinates) you have to let each decision have its own RNG So, if your problem has more than one decision (e.g., the pi problem had 2—x and y coordinates) you have to let each decision have its own RNG

16. 16 Quasi-random numbers (4) For a Monte Carlo process that has more than one decision, a different sequence must be used for each decision. (This is why we don’t use them.) For a Monte Carlo process that has more than one decision, a different sequence must be used for each decision. (This is why we don’t use them.) The most common way this situation is handled is to use prime numbers in order—2,3,5,7,11,etc.—for decisions. (“Halton” sequence, after UNC professor) The most common way this situation is handled is to use prime numbers in order—2,3,5,7,11,etc.—for decisions. (“Halton” sequence, after UNC professor) Asymptotic estimate of error is which means closer to ~ 1/N Asymptotic estimate of error is which means closer to ~ 1/N but deteriorates very quickly with dimensionality but deteriorates very quickly with dimensionality A Java subroutine is given in the public area. A Java subroutine is given in the public area. The resulting standard deviations calculated and printed by standard MC codes are NOT accurate The resulting standard deviations calculated and printed by standard MC codes are NOT accurate

17. 17 Overview of pdf and cdf Basic definition of probability distribution function (p.d.f.): Basic definition of probability distribution function (p.d.f.): And its integral, the cumulative distribution function (c.d.f.): And its integral, the cumulative distribution function (c.d.f.):

18. 18 Overview of pdf and cdf (2) Corollaries of these definitions: Corollaries of these definitions:

19. 19 Mapping  ->x using p(x) Our basic technique is to use a unique y->x Our basic technique is to use a unique y->x y=  from (0,1) and x from (a,b) y=  from (0,1) and x from (a,b) We are going to use the mapping backwards We are going to use the mapping backwards

20. 20 Mapping (2) Note that:  (a)=0  (a)=0  (b)=1  (b)=1 Function is non-decreasing over domain (a,b) Function is non-decreasing over domain (a,b) Our problem reduces to: Finding  (x) Finding  (x) Inverting to get x(  ), a formula for turning pseudo- random numbers into numbers distributed according to desired  (x) Inverting to get x(  ), a formula for turning pseudo- random numbers into numbers distributed according to desired  (x)

21. 21 Mapping (3) We must have: We must have:

22. 22 Resulting general procedure Form CDF: Form CDF: Set equal to pseudo-random number: Set equal to pseudo-random number: Invert to get formula that translates from  to x: Invert to get formula that translates from  to x:

23. 23 Uniform distribution For our first distribution, pick x uniformly in range (a,b): For our first distribution, pick x uniformly in range (a,b): Step 1: Form CDF. Step 1: Form CDF.

24. 24 Uniform distribution (2) Step 2: Set pseudo-random number to CDF: Step 2: Set pseudo-random number to CDF: Step 3: Invert to get x(  ): Step 3: Invert to get x(  ): Example: Choose  uniformly in (-1,1): Example: Choose  uniformly in (-1,1):

25. 25 Discrete distribution For a discrete distribution, we have N choices of state i, each with probability, so: For a discrete distribution, we have N choices of state i, each with probability, so: Step 1: Form CDF: Step 1: Form CDF:

26. 26 Discrete distribution (2) Step 2: Set pseudo-random number to CDF: Step 2: Set pseudo-random number to CDF: Step 3: Invert to get x(  ): Step 3: Invert to get x(  ):

27. 27 Discrete distribution (3) Example: Choose among 3 states with relative probabilities of 4, 5, and 6. Example: Choose among 3 states with relative probabilities of 4, 5, and 6.

28. 28 Continuous distribution: Direct This fits the “pure” form developed before. This fits the “pure” form developed before. Form CDF: Form CDF: Set equal to pseudo-random number: Set equal to pseudo-random number: Invert to get formula that translates from  to x: Invert to get formula that translates from  to x:

29. 29 Continuous: Direct (2) Example: Pick x from: Example: Pick x from:

30. 30 Homework from text

31. 31 Homework from text

32. 32 Homework from text

33. 33 Homework from text

34. 34 Homework from text

35. 35 Homework from text