# 11.1: Mathematical Expectation- Simple Random Variables

- Page ID
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The probability that real random variable \(X\) takes a value in a set \(M\) of real numbers is interpreted as the likelihood that the observed value \(X(\omega)\) on any trial will lie in \(M\). Historically, this idea of likelihood is rooted in the intuitive notion that if the experiment is repeated enough times the probability is approximately the fraction of times the value of \(X\) will fall in \(M\). Associated with this interpretation is the notion of the *average* of the values taken on. We incorporate the concept of *mathematical expectation* into the mathematical model as an appropriate form of such averages. We begin by studying the mathematical expectation of simple random variables, then extend the definition and properties to the general case. In the process, we note the relationship of mathematical expectation to the Lebesque integral, which is developed in abstract measure theory. Although we do not develop this theory, which lies beyond the scope of this study, identification of this relationship provides access to a rich and powerful set of properties which have far reaching consequences in both application and theory.

## Expectation for simple random variables

The notion of mathematical expectation is closely related to the idea of a weighted mean, used extensively in the handling of numerical data. Consider the arithmetic average \(\bar{x}\) of the following ten numbers: 1, 2, 2, 2, 4, 5, 5, 8, 8, 8, which is given by

\(\bar{x} = \dfrac{1}{10} (1 + 2 + 2 + 2 + 4 + 5 + 5 + 8 + 8 + 8)\)

Examination of the ten numbers to be added shows that five distinct values are included. One of the ten, or the fraction 1/10 of them, has the value 1, three of the ten, or the fraction 3/10 of them, have the value 2, 1/10 has the value 4, 2/10 have the value 5, and 3/10 have the value 8. Thus, we could write

\(\bar{x} = (0.1 \cdot 1 + 0.3 \cdot 2 + 0.1 \cdot 4 + 0.2 \cdot 5 + 0.3 \cdot 8)\)

The pattern in this last expression can be stated in words: Multiply each possible value by the fraction of the numbers having that value and then sum these products. The fractions are often referred to as the *relative frequencies*. A sum of this sort is known as a *weighted average*.

In general, suppose there are \(n\) numbers \(\{x_1, x_2, \cdot\cdot\cdot x_n\}\) to be averaged, with m≤nm≤n distinct values \(\{t_1, t_2 \cdot \cdot\cdot t_m\}\). Suppose \(f_1\) have value \(t_1\), \(f_2\) have value \(t_2\), \(\cdot\cdot\cdot\), \(f_m\) have value \(t_m\). The \(f_i\) must add to \(n\). If we set \(p_i = f_i / n\), then the fraction \(p_i\) is called the relative frequency of those numbers in the set which have the value \(t_i\), \(1 \le i \le m\). The average \(\bar{x}\) of the \(n\) numbers may be written

\(\bar{x} = \dfrac{1}{n} \sum_{i = 1}^{n} x_i = \sum_{j = 1}^{m} t_j p_j\)

In probability theory, we have a similar averaging process in which the relative frequencies of the various possible values of are replaced by the probabilities that those values are observed on any trial.

**Definition**. For a simple random variable \(X\) with values \(\{t_1, t_2, \cdot\cdot\cdot t_n\}\) and corresponding probabilities \(p_i = P(X = t_i)\) *mathematical expectation*, designated \(E[X]\), is the probability weighted average of the values taken on by \(X\). In symbols

\(E[X] = \sum_{i = 1}^{n} t_i P(X = t_i) = \sum_{i = 1}^{n} t_ip_i\)

Note that the expectation is determined by the distribution. Two quite different random variables may have the same distribution, hence the same expectation. Traditionally, this average has been called the *mean*, or the *mean value*, of the random variable \(X\).

Example 11.1.1. Some special cases

- Since \(X = aI_E = 0 I_{E^c} + aI_E\), we have \(E[aI_E] = a P(E)\).
- For \(X\) a constant \(c\), \(X = cI_{\Omega}\), so that \(E[c] = cP(\Omega) = c\).
- If \(X = \sum_{i = 1}^{n} t_i I_{A_i}\) then \(aX = \sum_{i = 1}^{n} at_i I_{A_i}\), so that

\(E[aX] = \sum_{i = 1}^{n} at_i P(A_i) = a\sum_{i = 1}^{n} t_i P(A_i) = aE[X]\)

Figure 1. Moment of a probability distribution about the origin.

**Mechanical interpretation**

In order to aid in visualizing an essentially abstract system, we have employed the notion of probability as mass. The distribution induced by a real random variable on the line is visualized as a unit of probability mass actually distributed along the line. We utilize the mass distribution to give an important and helpful mechanical interpretation of the expectation or mean value. In Example 6 in "Mathematical Expectation: General Random Variables", we give an alternate interpretation in terms of mean-square estimation.

Suppose the random variable \(X\) has values \(\{t_i; 1 \le i \le n\}\), with \(P(X = t_i) = p_i\). This produces a probability mass distribution, as shown in Figure 1, with point mass concentration in the amount of \(p_i\) at the point \(t_i\). The expectation is

\(\sum_{i} t_i p_i\)

Now |ti||ti| is the distance of point mass \(p_i\) from the origin, with \(p_i\) to the left of the origin iff \(t_i\) is negative. Mechanically, the sum of the products tipitipi is the *moment* of the probability mass distribution about the origin on the real line. From physical theory, this moment is known to be the same as the product of the total mass times the number which locates the center of mass. Since the total mass is one, the *mean value* is the *location of the center of mass*. If the real line is viewed as a stiff, weightless rod with point mass \(p_i\) attached at each value \(t_i\) of \(X\), then the mean value \(\mu_X\) is the point of balance. Often there are symmetries in the distribution which make it possible to determine the expectation without detailed calculation.

Example 11.1.2. the number of spots on a die

Let \(X\) be the number of spots which turn up on a throw of a simple six-sided die. We suppose each number is equally likely. Thus the values are the integers one through six, and each probability is 1/6. By definition

\(E[X] = \dfrac{1}{6} \cdot 1 + \dfrac{1}{6} \cdot 2 + \dfrac{1}{6} \cdot 3 + \dfrac{1}{6} \cdot 4 + \dfrac{1}{6} \cdot 5 + \dfrac{1}{6} \cdot 6 = \dfrac{1}{6} (1 + 2 + 3 + 4 + 5 + 6) = \dfrac{7}{2}\)

Although the calculation is very simple in this case, it is really not necessary. The probability distribution places equal mass at each of the integer values one through six. The center of mass is at the midpoint.

Example 11.1.3. a simple choice

A child is told she may have one of four toys. The prices are $2.50. $3.00, $2.00, and $3.50, respectively. She choses one, with respective probabilities 0.2, 0.3, 0.2, and 0.3 of choosing the first, second, third or fourth. What is the expected cost of her selection?

\(E[X] = 2.00 \cdot 0.2 + 2.50 \cdot 0.2 + 3.00 \cdot 0.3 + 3.50 \cdot 0.3 + 2.85\)

For a simple random variable, the mathematical expectation is determined as the dot product of the value matrix with the probability matrix. This is easily calculated using MATLAB.

matlab calculation for example 11.1.3

X = [2 2.5 3 3.5]; % Matrix of values (ordered) PX = 0.1*[2 2 3 3]; % Matrix of probabilities EX = dot(X,PX) % The usual MATLAB operation EX = 2.8500 Ex = sum(X.*PX) % An alternate calculation Ex = 2.8500 ex = X*PX' % Another alternate ex = 2.8500

**Expectation and primitive form**

The definition and treatment above assumes \(X\) is in *canonical form*, in which case

\(X = \sum_{i = 1}^{n} t_i I_{A_i}\), where \(A_i = \{X = t_i\}\), implies \(E[X] = \sum_{i = 1}^{n} t_i P(A_i)\)

We wish to ease this restriction to canonical form.

Suppose simple random variable \(X\) is in a *primitive form*

\(X = \sum_{j = 1}^{m} c_j I_{C_j}\), where \(\{C_j: 1 \le j \le m\}\) is a partition

We show that

\(E[X] = \sum_{j = 1}^{m} c_j P(C_j)\)

Before a formal verification, we begin with an example which exhibits the essential pattern. Establishing the general case is simply a matter of appropriate use of notation.

Example 11.1.4. simple random variable x in primitive form

\(X = I_{C_1} + 2I_{C_2} + I_{C_3} + 3 I_{C_4} + 2 I_{C_5} + 2I_{C_6}\), with \(\{C_1, C_2, C_3, C_4, C_5, C_6\}\) a partition

inspection shows the distinct possible values of \(X\) to be 1, 2, or 3. Also

\(A_1 = \{X = 1\} = C_1 \bigvee C_3\), \(A_2 = \{X = 2\} = C_2 \bigvee C_5 \bigvee C_6\) and \(A_3 = \{X = 3\} = C_4\)

so that

\(P(A-1) = P(C_1) + P(C_3)\), \(P(A_2) = P(C_2) + P(C_5) + P(C_6)\), and \(P(A_3) = P(C_4)\)

Now

\(E[X] = P(A_1) + 2P(A_2) + 3P(A_3) = P(C_1) + P(C_3) + 2[P(C_2) + P(C_5) + P(C_6)] + 3P(C_4)\)

\(= P(C_1) + 2P(C_2) + P(C_3) + 3P(C_4) + 2P(C_5) + 2P(C_6)\)

To establish the general pattern, consider \(X = \sum_{j = 1}^{m} c_j I_{C_j}\). We identify the distinct set of values contained in the set \(\{c_j: 1 \le j \le m\}\). Suppose these are \(t_1 < t_2 < \cdot\cdot\cdot < t_n\). For any value \(t_i\) in the range, identify the index set \(J_i\) of those \(j\) such that \(c_j = t_i\). Then the terms

\(\sum_{J_i} c_j I_{C_j} = t_i \sum_{J_i} I_{C_j} = t_i I_{A_i}\), where \(A_i = \bigvee_{j \in J_i} C_j\)

By the additivity of probability

\(P(A_i) = P(X = t_i) = \sum_{j \in J_i} P(C_j)\)

Since for each \(j \in J_i\) we have \(c_j = t_i\), we have

\(E[X] = \sum_{i = 1}^{n} t_i P(A_i) = \sum_{i = 1}^{n} t_i \sum_{j \in J_i} P(C_j) = \sum_{i = 1}^{n} \sum_{j \in J_i} c_j P(C_j) = \sum_{j = 1}^{m} c_j P(C_j)\)

— □

Thus, *the defining expression for expectation thus holds for X in a primitive form*.

An alternate approach to obtaining the expectation from a primitive form is to use the csort operation to determine the distribution of \(X\) from the coefficients and probabilities of the primitive form.

Example 11.1.5. Alternate determinations of E[x]

Suppose \(X\) in a primitive form is

\(X = I_{C_1} + 2 I_{C_2} + I_{C_3} + 3I_{C_4} + 2I_{C_5} + 2I_{C_6} + I_{C_7} + 3I_{C_8} + 2I_{C_9} + I_{C_{10}}\)

with respective probabilities

\(P(C_i) = 0.08, 0.11, 0.06, 0.13, 0.05, 0.08, 0.12, 0.07, 0.14, 0.16\)

c = [1 2 1 3 2 2 1 3 2 1]; % Matrix of coefficients pc = 0.01*[8 11 6 13 5 8 12 7 14 16]; % Matrix of probabilities EX = c*pc' EX = 1.7800 % Direct solution [X,PX] = csort(c,pc); % Determinatin of dbn for X disp([X;PX]') 1.0000 0.4200 2.0000 0.3800 3.0000 0.2000 Ex = X*PX' % E[X] from distribution Ex = 1.7800

**Linearity**

The result on primitive forms may be used to establish the *linearity* of mathematical expectation for simple random variables. Because of its fundamental importance, we work through the verification in some detail.

Suppose \(X = \sum_{i = 1}^{n} t_i I_{A_i}\) and \(Y = \sum_{j = 1}^{m} u_j I_{B_j}\) (both in canonical form). Since

\(\sum_{i = 1}^{n} I_{A_i} = \sum_{j = 1}^{m} I_{B_j} = 1\)

we have

\(X + Y = \sum_{i = 1}^{n} t_i I_{A_i} (\sum_{j = 1}^{m} I_{B_j}) + \sum_{j = 1}^{m} u_j I_{B_j} (\sum_{i = 1}^{n} I_{A_i}) = \sum_{i = 1}^{n} \sum_{j = 1}^{m} (t_i + u_j) I_{A_i} I_{B_j}\)

Note that \(I_{A_i} I_{B_j} = I_{A_i B_j}\) and \(A_i B_j = \{X = t_i, Y = u_j\}\). The class of these sets for all possible pairs \((i, j)\) forms a partition. Thus, the last summation expresses \(Z = X + Y\) in a primitive form. Because of the result on primitive forms, above, we have

\(E[X + Y] = \sum_{i = 1}^{n} \sum_{j = 1}^{m} (t_i + u_j) P(A_i B_j) = \sum_{i = 1}^{n} \sum_{j = 1}^{m} t_i P(A_i B_j) + \sum_{i = 1}^{n} \sum_{j = 1}^{m} u_j P(A_i B_j)\)

\(= \sum_{i = 1}^{n} t_i \sum_{j = 1}^{m} P(A_i B_j) + \sum_{j = 1}^{m} u_j \sum_{i = 1}^{n} P(A_i B_j)\)

We note that for each \(i\) and for each \(j\)

\(P(A_i) = \sum_{j = 1}^{m} P(A_i B_j)\) and \(P(B_j) = \sum_{i = 1}^{n} P(A_i B_j)\)

Hence, we may write

\(E[X + Y] = \sum_{i = 1}^{n} t_i P(A_i) + \sum_{j = 1}^{m} u_j P(B_j) = E[X] + E[Y]\)

Now \(aX\) and \(bY\) are simple if \(X\) and \(Y\) are, so that with the aide of Example 11.1.1 we have

\(E[aX + bY] = E[aX] + E[bY] = aE[X] + bE[Y]\)

If \(X, Y, Z\) are simple, then so are \(aX + bY\), and \(cZ\). It follows that

\(E[aX + bY + cZ] = E[aX + bY] + cE[Z] = aE[X] + bE[Y] + cE[Z]\)

By an inductive argument, this pattern may be extended to a linear combination of any finite number of simple random variables. Thus we may assert

**Linearity**. The expectation of a linear combination of a finite number of simple random variables is that linear combination of the expectations of the individual random variables.

— □

**Expectation of a simple random variable in affine form**

As a direct consequence of linearity, whenever simple random variable \(X\) is in affine form, then

\(E[X] = E[c_0 + \sum_{i = 1}^{n} c_i I_{E_i}] = c_0 + \sum_{i = 1}^{n} c_i P(E_i)\)

*Thus, the defining expression holds for any affine combination of indicator functions, whether in canonical form or not.*

Example 11.1.6. binomial distribution (n,p)

This random variable appears as the number of successes in \(n\) Bernoulli trials with probability *p* of success on each component trial. It is naturally expressed in affine form

\(X = \sum_{i = 1}^{n} I_{E_i}\) so that \(E[X] = \sum_{i = 1}^{n} p = np\)

Alternately, in canonical form

\(X = \sum_{k = 0}^{n} k I_{A_{kn}}\), with \(p_k = P(A_{kn}) = P(X = k) = C(n, k) p^{k} q^{n - k}\), \(q = 1 - p\)

so that

\(E[X] = \sum_{k = 0}^{n} kC(n, k) p^k q^{n - k}\), \(q = 1 - p\)

Some algebraic tricks may be used to show that the second form sums to \(np\), but there is no need of that. The computation for the affine form is much simpler.

Example 11.1.7. Expected winnings

A bettor places three bets at $2.00 each. The first bet pays $10.00 with probability 0.15, the second pays $8.00 with probability 0.20, and the third pays $20.00 with probability 0.10. What is the expected gain?

**Solution**

The net gain may be expressed

\(X = 10I_A + 8I_B + 20 I_C - 6\), with \(P(A) = 0.15\), \(P(B) = 0.20\), \(P(C) = 0.10\)

Then

\(E[X] = 10 \cdot 0.15 + 8 \cdot 0.20 + 20 \cdot 0.10 - 6 = -0.90\)

These calculations may be done in MATLAB as follows:

c = [10 8 20 -6]; p = [0.15 0.20 0.10 1.00]; % Constant a = aI_(Omega), with P(Omega) = 1 E = c*p' E = -0.9000

**Functions of simple random variables**

If \(X\) is in a primitive form (including canonical form) and \(g\) is a real function defined on the range of \(X\), then

\(Z = g(X) = \sum_{j = 1}^{m} g(c_j) I_{C_j}\) a primitive form

so that

\(E[Z] = E[g(X)] = \sum_{j = 1}^{m} g(c_j) P(C_j)\)

Alternately, we may use csort to determine the distribution for \(Z\) and work with that distribution.

*Caution*. If \(X\) is in affine form (but not a primitive form)

\(X = c_0 + \sum_{j = 1}^{m} c_j I_{E_j}\) then \(g(X) \ne g(c_0) + \sum_{j = 1}^{m} g(c_j) I_{E_j}\)

so that

\(E[g(X)] \ne g(c_0) + \sum_{j = 1}^{m} g(c_j) P(E_j)\)

Example 11.1.8. expectation of a function of x

Suppose \(X\) in a primitive form is

\(X = -3I_{C_1} - I_{C_2} + 2I_{C_3} - 3I_{C_4} + 4I_{C_5} - I_{C_6} + I_{C_7} + 2I_{C_8} + 3I_{C_9} + 2I_{C_{10}}\)

with probabilities \(P(C_i) = 0.08, 0.11, 0.06, 0.13, 0.05, 0.08, 0.12, 0.07, 0.14, 0.16\).

Let \(g(t) = t^2 +2t\). Determine \(E(g(X)]\).

c = [-3 -1 2 -3 4 -1 1 2 3 2]; % Original coefficients pc = 0.01*[0 11 6 13 5 8 12 7 14 16]; % Probabilities for C_j G = c.^2 + 2*c % g(c_j) G = 3 -1 8 3 24 -1 3 8 15 8 EG = G*pc' % Direct computation EG = 6.4200 [Z,PZ] = csort(G,pc); % Distribution for Z = g(X) disp([Z; PZ]') -1.0000 0.1900 3.0000 0.3300 8.0000 0.2900 15.0000 0.1400 24.0000 0.0500 EZ = Z*PZ' % E[Z] from distribution for Z EZ = 6.4200

A similar approach can be made to a function of a pair of simple random variables, provided the joint distribution is available. Suppose \(X = \sum_{i = 1}^{n} t_i I_{A_i}\) and \(Y = \sum_{j = 1}^{m} u_j I_{B_j}\) (both in canonical form). Then

\(Z = g(X,Y) = \sum_{i = 1}^{n} \sum_{j = 1}^{m} g(t_i, u_j) I_{A_i B_j}\)

The \(A_i B_j\) form a partition, so \(Z\) is in a primitive form. We have the same two alternative possibilities: (1) direct calculation from values of \(g(t_i, u_j)\) and corresponding probabilities \(P(A_i B_j) = P(X = t_i, Y = u_j)\), or (2) use of csort to obtain the distribution for \(Z\).

Example 11.1.9. expectation for z = g(x,y)

We use the joint distribution in file jdemo1.m and let \(g(t, u) = t^2 + 2tu - 3u\). To set up for calculations, we use jcalc.

% file jdemo1.m X = [-2.37 -1.93 -0.47 -0.11 0 0.57 1.22 2.15 2.97 3.74]; Y = [-3.06 -1.44 -1.21 0.07 0.88 1.77 2.01 2.84]; P = 0.0001*[ 53 8 167 170 184 18 67 122 18 12; 11 13 143 221 241 153 87 125 122 185; 165 129 226 185 89 215 40 77 93 187; 165 163 205 64 60 66 118 239 67 201; 227 2 128 12 238 106 218 120 222 30; 93 93 22 179 175 186 221 65 129 4; 126 16 159 80 183 116 15 22 113 167; 198 101 101 154 158 58 220 230 228 211];

jdemo1 % Call for data jcalc % Set up Enter JOINT PROBABILITIES (as on the plane) P Enter row matrix of VALUES of X X Enter row matrix of VALUES of Y Y Use array operations on matrices X, Y, PX, PY, t, u, and P G = t.^2 + 2*t.*u - 3*u; % Calculation of matrix of [g(t_i, u_j)] EG = total(G.*P) % Direct claculation of expectation EG = 3.2529 [Z,PZ] = csort(G,P); % Determination of distribution for Z EZ = Z*PZ' % E[Z] from distribution EZ = 3.2529