The Uniform Probability Law

The notion of the uniform probability law (or uniform distribution) over the interval to , in which and are finite real numbers, is best defined in the following manner. Consider a numerical valued random phenomenon whose values can lie only in a certain finite interval ; that is real numbers for some finite numbers and . The random phenomenon is said to obey a uniform probability law over the finite interval if the value of its probability function, at any interval , satisfies the relation

\begin{align} P[B] = \begin{cases} \frac{\text{length of } B}{\text{length of } S}, & \text{if } B \text{ is a subset of } S \Misplaced &2mm] 0, & \text{if } B \text{ and } S \text{ have no points in common.} \end{cases} \tag{5.1} \end{align} </span></p><p>It should be noted that knowing <mjx-container aria-label="P[B]" class="MathJax" jax="SVG"><svg style="vertical-align: -0.566ex;" xmlns="http://www.w3.org/2000/svg" width="4.674ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 2066 1000" xmlns:xlink="http://www.w3.org/1999/xlink"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><use data-c="1D443" xlink:href="#MJX-TEX-I-1D443"></use></g><g data-mml-node="mo" transform="translate(751,0)"><use data-c="5B" xlink:href="#MJX-TEX-N-5B"></use></g><g data-mml-node="mi" transform="translate(1029,0)"><use data-c="1D435" xlink:href="#MJX-TEX-I-1D435"></use></g><g data-mml-node="mo" transform="translate(1788,0)"><use data-c="5D" xlink:href="#MJX-TEX-N-5D"></use></g></g></g></svg></mjx-container>at intervals suffices to determine it on any Borel set <mjx-container aria-label="B" class="MathJax" jax="SVG"><svg style="vertical-align: 0;" xmlns="http://www.w3.org/2000/svg" width="1.717ex" height="1.545ex" role="img" focusable="false" viewBox="0 -683 759 683" xmlns:xlink="http://www.w3.org/1999/xlink"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><use data-c="1D435" xlink:href="#MJX-TEX-I-1D435"></use></g></g></g></svg></mjx-container>of real numbers.</p><p>From <a href="https://adaptivebooks.org/probability-theory-and-its-applications/numerical-valued-random-phenomena/the-uniform-probability-law#Eq:4.5.1">(5.1) </a>one sees that <i>the notion of a uniform distribution represents an extension of the notion of a finite sample description space <mjx-container aria-label="S" class="MathJax" jax="SVG"><svg style="vertical-align: -0.05ex;" xmlns="http://www.w3.org/2000/svg" width="1.459ex" height="1.645ex" role="img" focusable="false" viewBox="0 -705 645 727" xmlns:xlink="http://www.w3.org/1999/xlink"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><use data-c="1D446" xlink:href="#MJX-TEX-I-1D446"></use></g></g></g></svg></mjx-container>with equally likely descriptions </i>, since in this case the probability <mjx-container aria-label="P[A]" class="MathJax" jax="SVG"><svg style="vertical-align: -0.566ex;" xmlns="http://www.w3.org/2000/svg" width="4.654ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 2057 1000" xmlns:xlink="http://www.w3.org/1999/xlink"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><use data-c="1D443" xlink:href="#MJX-TEX-I-1D443"></use></g><g data-mml-node="mo" transform="translate(751,0)"><use data-c="5B" xlink:href="#MJX-TEX-N-5B"></use></g><g data-mml-node="mi" transform="translate(1029,0)"><use data-c="1D434" xlink:href="#MJX-TEX-I-1D434"></use></g><g data-mml-node="mo" transform="translate(1779,0)"><use data-c="5D" xlink:href="#MJX-TEX-N-5D"></use></g></g></g></svg></mjx-container>of any event <mjx-container aria-label="A" class="MathJax" jax="SVG"><svg style="vertical-align: 0;" xmlns="http://www.w3.org/2000/svg" width="1.697ex" height="1.62ex" role="img" focusable="false" viewBox="0 -716 750 716" xmlns:xlink="http://www.w3.org/1999/xlink"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><use data-c="1D434" xlink:href="#MJX-TEX-I-1D434"></use></g></g></g></svg></mjx-container>on <mjx-container aria-label="S" class="MathJax" jax="SVG"><svg style="vertical-align: -0.05ex;" xmlns="http://www.w3.org/2000/svg" width="1.459ex" height="1.645ex" role="img" focusable="false" viewBox="0 -705 645 727" xmlns:xlink="http://www.w3.org/1999/xlink"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><use data-c="1D446" xlink:href="#MJX-TEX-I-1D446"></use></g></g></g></svg></mjx-container>is given by the formula</p><p><span class="math display" id="Eq:4.5.2" label="Eq:4.5.2">\[P[A]=\frac{\text { size of } A}{\text { size of } S}. \tag{5.2} 

There are many random phenomena for which it appears plausible to assume a uniform probability law. For example, suppose one is tossing a dart at a line marked 0 to 1. If one is always sure to land on the line and if one feels that , then one is led to conclude that the place at which the dart hits the line has a probability function satisfying (5.1) , with denoting the interval 0 to 1.

The distribution function of a random phenomenon, which obeys a uniform probability law over the interval to is obtained from (5.1) :

\begin{align} F(x) = \begin{cases} 0, & \text{if } x \leq a \\[2mm] \frac{x-a}{b-a}, & \text{if } a \leq x \leq b \\[2mm] 1, & \text{if } x \geq b. \end{cases} \tag{5.3} \end{align} 

By differentiation, the probability density function may be obtained:

\begin{align} f(x) = \begin{cases} \frac{1}{b-a}, & \text{if } a < x < b \\[2mm] 0, & \text{otherwise.} \end{cases} \tag{5.4} \end{align} 

From (5.4) it follows that the definition of a uniform probability law given by (5.1) coincides with the definition given by (4.10) . (See (Fig. 5A) )

Figure 2.4.1
Fig. 5A. Probability density function and distribution function of the uniform probability law over (a) the interval 1 to 1.5, (b) the interval 1 to 3.

Example 5A . Waiting time for a train . Between 7 A.M. and 8 A.M. trains leave a certain station at minutes past the hour. What is the probability that a person arriving at the station will have to wait less than a minute for a train, assuming that the person’s time of arrival at the station obeys a uniform probability law over the interval of time (i) 7 A.M. to 8 A.M., (ii) 7:15 A.M. to 7: 30 A.M., (iii) 7:02 A.M. to 7:15 A.M., (iv) 7:03 A.M. to 7:15 A.M., (v) 7:04 A.M. to 7:15 A.M.?

 

Solution

We must first find the set of real numbers in which the person’s arrival must lie in order for his waiting time to be less than 1 minute. One sees that is the set of real numbers consisting of the intervals 2 to 3, 4 to 5, 7 to 8, 9 to 10, and so on. (See Fig. 5B .) The probability that the person will wait less than a minute for a train is given by , which is equal to, in the various cases, (i) , (ii) , (iii) , (iv) , (v) .

 

Figure 2.4.1

Fig. 5B . In order that the person discussed in example 5A will have to wait less than one minute for a train, his time of arrival at the station (measured in minutes after 7 A.M.) must lie in the set , consisting of the shaded intervals.

 

Example 5B . The probability law of the second digit in the decimal expansion of the square root of a randomly chosen number . A number is chosen from the interval 0 to 1 by a random mechanism that obeys a uniform probability law over the interval. What is the probability that the second decimal place of the square root of the number will be the digit 3? Is the digit for ?

 

Solution

For let be the set of numbers on the unit interval whose square roots have a second decimal equal to the digit . A number belongs to if and only if satisfies for some ,

 

or

The length of the interval described by (5.5) is

Hence the probability of the set is given by

In particular, .

Exercises

5.1 . The time, measured in minutes, required by a certain man to travel from his home to a train station is a random phenomenon obeying a uniform probability law over the interval 20 to 25. If he leaves his home promptly at 7:05 A.M., what is the probability that he will catch a train that leaves the station promptly at 7:28 A.M.?

 

Answer

.

 

5.2 . A radio station broadcasts the correct time every hour on the hour between the hours of 6 A.M. and 12 midnight. What is the probability that a listener will have to wait less than 10 minutes to hear the correct time if the time at which he tunes in is distributed uniformly over (chosen randomly from) the interval (i) 6 A.M. to 12 midnight, (ii) 8 A.M. to 6 P.M., (iii) 7:30 A.M. to 5:30 P.M., (iv) 7:30 A.M. to 5 P.M?

5.3 . The circumference of a wheel is divided into 37 arcs of equal length, which are numbered 0 to 36 (this is the principle of construction of a roulette wheel). The wheel is twirled. After the wheel comes to rest, the point on the wheel located opposite a certain fixed marker is noted. Assume that the point thus chosen obeys a uniform probability law over the circumference of the wheel. What is the probability that the point thus chosen will lie in an arc (i) with a number 1 to 10, inclusive, (ii) with an odd number, (iii) numbered 0?

 

Answer

(i) ; (ii) ; (iii) .

 

5.4 . A parachutist lands on the line connecting 2 towns, and . Suppose that the point at which he lands obeys a uniform probability law over the line. What is the probability that the ratio of his distance from to his distance from will be (i) greater than 3, (ii) equal to 3, (iii) greater than , where is a given real number?

5.5 . An angle is chosen from the interval to by a random mechanism that obeys a uniform probability law over the interval. A line is then drawn on an -plane through the point at the angle with the -axis. What is the probability, for any positive number , that the -coordinate of the point at which the line intersects the -axis will be less than ?

 

Answer

.

 

5.6 . A number is chosen from the interval 0 to 1 by a random mechanism that obeys a uniform probability law over the interval. What is the probability that (i) its first decimal will be a 3, (ii) its second decimal will be a 3, (iii) its first 2 decimals will be 3’s, (iv) any specified decimal will be a 3, (v) any 2 specified decimals will be 3’s?

5.7 . A number is chosen from the interval 0 to 1 by a random mechanism that obeys a uniform probability law over the interval. What is the probability that (i) the first decimal of its square root will be a 3, (ii) the negative of its logarithm (to the base ) will be less than 3?

 

Answer

(i) ; (ii) .