Description
INTRODUCTION
A stochastic variable or probabilistic variable is a variable which value is non deterministic. For example a hdice outcomei can be a part of a formula (probably a game theoretical one) as a stochastic variable. As you would agree, the variable will have integer values in the range [1,6] with equal probabilities of 1=6. So if we would plot the function of the outcome value versus probability we would have:
Proper dice outcome probability
1/2
5/12
1/3
1/4
1/6
1/12
0
1 2 3 4 5 6
Biased dice outcome probability
-
-
-
-
-
1/2
5/12
1/3
1/4
1/6
1/12
0
1
2
3
4
5
6
-
-
-
-
The sum of all probabilities though would still be 1 (unity). In other words the probabilistic function is normalized to 1. These functions de ne the stochastic variable h dice outcomei as a set of possible values each of which occurs with a certain probability (de ned by the function).
Many probabilities are depending of the value of the outcome. For example, based on demographic work, if a male person is going to die in the age range of [15,79] then the probability varying with the age:
Male death probability
-
-
-
-
0.4
0.36
0.32
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
15-19
20-24
25-29
30-34
35-39 40-44 45-49
50-54
55-59
60-64
65-69
70-74
75-79
AGE
-
-
-
Certainly, the increase in the functions of the examples are pure coincidence. The function can have any shape: for example the shape of the normal distribution, which is found in many natural and social events is of a shape of a bell.
Now what if this type of variable participate a function? What can we say about the result of the function? It will be some probability distribution function. Depending on the possible values the stochastic (ingradient) variables take the function will take some values. Depending on the probability of those possible values the probability of the outcome will shape.
PROBLEM
How can this probability distribution function be calculated? This is the task of this take home exam.
If we have the chance to perform repetitive experimentation of considerable amount then we can de-termine the distribution by performing random generated experimentation and calculate the outcome for the function. We will do so by ‘drawing’ values for each variable with probabilities set by their associated probability distribution functions. Then we substitute these values for the variables and do a calculation of the formula that de nes the function. We record the resulting value. Repeating over and over again this task will give us a histogram of the outcome values. The last task is to convert the histogram into a probability distribution by normalizing it.
In the input you will be given an in x formula that de nes the function we are going to compute the probability distribution function of. Furthermore, for each variable in the formula you will be given an ordered set of n tuples. Each tuple is a value and the probability that value will occur. The probabilities will be normalized (ie. they add up to unity).
Here is the BNF representation for the formula:
-
hformulai
::= hformulai hoperatori hformulai j
( hformulai ) j
hfunctioni ( hformulai ) j
hletteri j hunsigned numberi
hoperatori
::= + j – j * j / j ^
hfunctioni
::= ~ j sin j cos j sqrt j ln
hletteri
::= A j B j : : : j Z
hunsigned numberi
::= hany unsigned %lf readable numberi
As far as semantic is concerned:
+, -, *, /, have conventional meaning, associativity and precedence (as of C). ^ is exponentiation, has right associativity and top precedence among all hoperatoris. For simplicity unary minus, represented by ~ and subtraction have di erent symbols. Furthermore, again for simplicity, unary minus is a hfunctioni (is followed by a compulsory pair of parenthesis). Note that we do not have direct negative number input (like -425.).
SPECIFICATIONS
The hformulai will be the rst line of the input and will not extend over the following lines. Any subpart of the input that is also a hformulai may be (pre/pro)ceeded by blank(s). Your implementation must be insensitive to them.
The maximum length of the formula line is 200 characters.
The following line contains two parameters (separated by blank(s)):
{ The count of intervals n for the probability distribution functions (also valid for the result). This is an integer in the range [5; 100].
{ Count of experiments. This is an long int.
Each of the lines starting with the third line and following it, is of the structure: { a hletteri, followed by blank(s).
{ Two oating point values, the lower limit of the value axis (the x-axis) of the distribution function for the hletteri variable and the upper limit, respectively.
{ n probabilities. The rst is the probability corresponding to the rst interval, the second is the is the probability corresponding to the second interval, and so on.
In this manner the probability distributions for all the variables will be provided. As observed, the intervals are of equal length.
Furthermore, the probability among a single interval is constant. So, for example let us have an interval [50; 59] a probability assigned that is 0.015 . This says
hcount of values in the range [50,59]i = 0:015
hcount of all valuesi
and the probability of having, for example, 50; 50:1; 53; 58:99 or 59 are all the same (attn: of course each of those individual probabilities are not 0.015).
The output is a line with a content similar to a line describing a variable. But this time we do not have the letter. The output starts with the lower and upper limit oating points, followed by n probability values. This is the computed probability distribution for the formula input. All output goes to a single line.
There will be no erroneous input.
Here is an input example:
|
sqrt(2 – sin(3*A/B)^2.5) |
+ 0.5*(C*~(D) + |
3.11 |
+B) |
||||||||||||
|
20 10000 |
|||||||||||||||
|
C 3.25 18. |
0.01 |
.01 .02 .04 |
.08 .02 .02 .05 |
.065 .08 |
.1 .13 .2 |
.05 .04 .04 .03 .01 |
.005 |
.0 |
|||||||
|
A 0 7.5 .054 .031 .016 .008 |
.116 .124 .147 |
.155 |
.039 |
.023 .016 |
.008 .124 |
.062 |
.031 |
.016 |
.008 .008 .008 .006 |
||||||
|
D -1.5 0.5 |
.012 |
.025 |
.05 .1 |
.1 |
.1 .025 .012 |
0 |
0 |
0 .012 .025 .1 |
.2 |
.1 .05 |
.039 |
.025 |
.025 |
||
|
B13.117 |
.058 |
.029 |
.015 .007 |
.007 .007 .015 |
.022 .029 .036 .044 |
.051 .058 .066 .073 .080 .088 .095 .103 |
|||||||||
HINTS & HOW-TO-DO’S
Your rst task should be to discover how the Dijkstra’s algorithm and the evaluation phase is going to be altered to handle hfunctioni. (There is a simple, though elegant solution to this). To brush up your memory you may consult your CENG 111 course book.
Write a draw function which takes as parameter your internal representation of a probability dis-tribution and generates random values conforming with the probability distribution. You have the random() function available in stdlib.h to hand (man random is your friend). You have to gure out a way to get a randomization proportional to the probability values. Imagine a blind man making random dots with his pencil on a ruler…
You do not have the chance to know what the lower and upper bounds of the outcome is. So you do not have a way to know what the intervals are. You can determine these only after the completion of all the experiments. So what to do? Think about it.
