# Set Theory Basics

## Venn diagram, universal and empty set

Set representation in the Venn diagram

In later chapters,  we will sometimes refer to  set theory .  Therefore,  the most important basics and definitions of this discipline will be briefly summarized here.  The topic is also covered in the  (German language)  learning video
"Mengentheoretische Begriffe und Gesetzmäßigkeiten"   ⇒   "Set Theory – Terms and Regularities"

An important tool of set theory is the  Venn diagramm  according to the graph:

• Applied to probability theory,  here the events  $A_i$  are represented as areas.  For a simpler description we do not denote the events here with  $A_1$,  $A_2$  and  $A_3$,  but with  $A$,  $B$  and  $C$ in contrast to the last chapter.
• The total area corresponds to the  "universal set"  (or short:  "universe")  $G$.  The universe  $G$  contains all possible outcomes and stands for the  certain event,  which by definition occurs with probability „one”:   ${\rm Pr}(G) = 1$.  For example,  in the random experiment  "Throwing a die",  the probability for the event  "The number of eyes is less than or equal to 6"  is identical to one.
• In contrast,  the  empty set  $ϕ$  does not contain a single element.  In terms of events,  the empty set specifies the  impossible event  with probability  ${\rm Pr}(ϕ) = 0$  an.  For example,  in the experiment  "Throwing a die",  the probability for the event  "The number of eyes is greater than 6" is identically zero.

Note that not every event  $A$  with  ${\rm Pr}(A) = 0$  can really never happen.  For example:

• The event  "The noise value  $n$  is identically zero"  is vanishingly small and  ${\rm Pr}(n \equiv 0) = 0$,  if  $n$  is described by a continuous  (Gaussian)  random variable.
• Nevertheless,  it is of course possible  (although extremely unlikely)  that at some point the exact noise value  $n = 0$  will also occur.

## Union set

Some set-theoretical relationss are explained now on the basis of the Venn diagram.

$\text{Definition:}$  The  union set  $C$  of two sets  $A$  and  $B$  contains all the elements that are contained either in set  $A$  or in set  $B$  or in both.

• This relationship is expressed as the following formula:
$$\ C = A \cup B.$$
Union set in the Venn diagram

Using the diagram, it is easy to see the following laws of set theory:

$$A \cup \it \phi = A \rm \hspace{3.6cm}(union \hspace{0.15cm}with \hspace{0.15cm}the \hspace{0.15cm}empty \hspace{0.15cm}set),$$
$$A\cup G = G \rm \hspace{3.6cm}(union \hspace{0.15cm}with \hspace{0.15cm}the \hspace{0.15cm}universe),$$
$$A\cup A = A \hspace{3.6cm}(\rm tautology),$$
$$A\cup B = B\cup A \hspace{2.75cm}(\rm commutative \hspace{0.15cm}property),$$
$$(A\cup B)\cup C = A\cup (B\cup C) \hspace{0.45cm}(\rm associative \hspace{0.15cm}property).$$

If nothing else is known about the event sets  $A$  and  $B$  then only a lower bound and an upper bound can be given for the probability of the union set:

$${\rm Max}\big({\rm Pr} (A), \ {\rm Pr} (B)\big) \le {\rm Pr} (A \cup B) \le {\rm Pr} (A)+{\rm Pr} (B).$$
• The probability of the union set is equal to the lower bound if  $A$  is a  subset  of  $B$  or vice versa.
• The upper bound holds for  disjoint sets.

$\text{Example 1:}$  We consider again the experiment  "throwing a die".  The possible outcomes  (number of points)  are thus  $E_μ ∈ G = \{1, 2, 3, 4, 5, 6\}$.

Consider the two events

• $A :=$  "The outcome is greater than or equal to  $5$"$= \{5, 6\}$   ⇒   ${\rm Pr} (A)= 2/6= 1/3$,
• $B :=$  "The outcome is even" $= \{2, 4, 6\}$   ⇒   ${\rm Pr} (B)= 3/6= 1/2$,

then the union set contains four elements:   $(A \cup B) = \{2, 4, 5, 6 \}$   ⇒   ${\rm Pr} (A \cup B) = 4/6 = 2/3$.

• For the lower bound:   ${\rm Pr} (A \cup B) \ge {\rm Max}\big({\rm Pr} (A),\ {\rm Pr} (B)\big ) = 3/6.$
• For the upper bound:   ${\rm Pr} (A \cup B) \le {\rm Pr} (A)+{\rm Pr} (B) = 5/6.$

## Intersection set

Another important set-theoretic relation is the intersection.

$\text{Definition:}$  The  intersection set  $C$  of two sets  $A$  and  $B$  contains all those elements which are contained in both the set  $A$  and the set  $B$.

• This relationship is expressed as the following formula:
$$C = A \cap B.$$

Intersection set in the Venn diagram

In the diagram,  the intersection is shown in purple.  Analog to the union set,  the following regularities apply here:

$$A \cap \it \phi = \it \phi \rm \hspace{3.75cm}(intersection \hspace{0.15cm}with \hspace{0.15cm}the \hspace{0.15cm}empty \hspace{0.15cm}set),$$
$$A \cap G = A \rm \hspace{3.6cm}(intersection \hspace{0.15cm}with \hspace{0.15cm}the \hspace{0.15cm}universe),$$
$$A\cap A = A \rm \hspace{3.6cm}(tautology),$$
$$A\cap B = B\cap A \rm \hspace{2.75cm}(commutative \hspace{0.15cm}property),$$
$$(A\cap B)\cap C = A\cap (B\cap C) \rm \hspace{0.45cm}(associative \hspace{0.15cm}property).$$
• If nothing else is known about  $A$  and  $B$,  then no statement can be made for the probability of the intersection.
• However,  if  ${\rm Pr} (A) \le 1/2$  and at the same time  ${\rm Pr} (B) \le 1/2$ hold,  then a lower and an upper bound can be given:
$$0 \le {\rm Pr} (A ∩ B) \le {\rm Min}\ \big({\rm Pr} (A),\ {\rm Pr} (B)\big ).$$
• ${\rm Pr}(A ∩ B)$  is sometimes called the  "joint probability"  and is denoted by  ${\rm Pr}(A, \ B)$.
• ${\rm Pr}(A ∩ B)$  is equal to the upper bound if  $A$  is a  subset  of  $B$  or vice versa.
• The lower bound is obtained for the joint probability of  disjoint sets.

$\text{Example 2:}$  We consider again the experiment  "throwing a die".  The possible outcomes  (number of points)  are thus  $E_μ ∈ G = \{1, 2, 3, 4, 5, 6\}$.

Consider the two events

• $A :=$  "The outcome is greater than or equal to  $5$" $= \{5, 6\}$   ⇒   ${\rm Pr} (A)= 2/6= 1/3$,  and
• $B :=$  "The outcome is even" $= \{2, 4, 6\}$   ⇒   ${\rm Pr} (B)= 3/6= 1/2$.

The intersection contains only one element:   $(A ∩ B) = \{ 6 \}$   ⇒   ${\rm Pr} (A ∩ B) = 1/6$.

• The upper bound is obtained as  ${\rm Pr} (A ∩ B) \le {\rm Min}\ \big ({\rm Pr} (A), \, {\rm Pr} (B)\big ) = 2/6.$
• The lower bound of the intersection here is zero because of  ${\rm Pr} (A) \le 1/2$  and  ${\rm Pr} (B) \le 1/2$ .

## Complementary set

$\text{Definition:}$  The  complementary set of  $A$  is often denoted by a straight line above the letter  $(\overline{A})$ .  It contains all the elements that are not contained in the set  $A$  and it holds for their probability:

$${\rm Pr}(\overline{A}) = 1- {\rm Pr}(A).$$

Complementary set in the Venn diagram

In the Venn diagram,  the complementary to  $A$  is shaded.  From this diagram, some set-theoretic relationships can be seen:

• The complementary of the complementary of  $A$  is the set  $A$  itself:
$$\overline{\overline{A}} = A.$$
• The union of a set  $A$  with its complementary set gives the universal set:
$${\rm Pr}(A \cup \overline{A}) = {\rm Pr}(G) = \rm 1.$$
• The intersection of  $A$  with its complementary set gives the empty set:
$${\rm Pr}(A \cap \overline{A}) = {\rm Pr}({\it \phi}) \rm = 0.$$

$\text{Example 3:}$  We consider again the experiment  "throwing a die".  The possible outcomes  (number of points)  are thus  $E_μ ∈ G = \{1, 2, 3, 4, 5, 6\}$.

Starting from the set

• $A :=$  "The outcome is smaller than  $5$" $= \{1, 2, 3, 4\}$   ⇒   ${\rm Pr} (A)= 2/3$,

the corresponding complementary set is

• $\overline{A} :=$  "The outcome is greater than or equal to  $5$" $= \{5, 6\}$   ⇒   ${\rm Pr} (\overline{A})= 1 - {\rm Pr} (A) = 1 - 2/3 = 1/3.$

## Proper subset – Improper subset

Proper subset in the Venn diagram

$\text{Definitions:}$

(1)  One calls  $A$  a  proper subset  of  $B$  and writes for this relationship  $A ⊂ B$,

• if all elements of  $A$  are also contained in  $B$,
• but not all elements of  $B$  are contained in  $A$.

In this case,  the probabilities are:

$${\rm Pr}(A) < {\rm Pr}(B).$$

This set-theoretic relationship is illustrated by the sketched Venn diagram on the right.

(2)  On the other hand,  $A$  is called an  improper subset  of  $B$  and uses the notation

$$A \subseteq B = (A \subset B) \cup (A = B),$$

if  $A$  is either a proper subset of  $B$  or if  $A$  and  $B$  are equal sets.

• The relation  ${\rm Pr} (A) \le {\rm Pr} (B)$  then applies to the probabilities.
• The equality sign is only valid for the special case  $A = B$.

In addition,  however,  the two equations known as the  absorption laws  also apply:

$$(A \cap B) \cup A = A ,$$
$$(A \cup B) \cap A = A,$$

since the intersection  $A ∩ B$  is always a subset of  $A$,  but at the same time  $A$  is also a subset of the union  $A ∪ B$.

$\text{Example 4:}$  We consider again the experiment  "throwing a die".  The possible outcomes  (number of points)  are thus  $E_μ ∈ G = \{1, 2, 3, 4, 5, 6\}$.

Consider the two events

• $A :=$  "The outcome is odd" $= \{1, 3, 5\}$   ⇒   ${\rm Pr} (A)= 3/6$,  and
• $B :=$  "The outcome is a prime number" $= \{1, 2, 3, 5\}$   ⇒   ${\rm Pr} (B)= 4/6$.

It can be seen that  $A$  is a  (proper) subset  of  $B$ .  Accordingly,  ${\rm Pr} (A) < {\rm Pr} (B)$  is also true.

## Theorems of de Morgan

In many set-theoretical tasks,  the two theorems of  de Morgan  are extremely useful.  These are:

$$\overline{A \cup B} = \overline{A} \cap \overline{B},$$
$$\overline{A \cap B} = \overline{A} \cup \overline{B}.$$

These regularities are illustrated in the Venn diagram:

• Set  $A$  is shown in red and set  $B$  is shown in blue.
• The compliment  $\overline {A}$  of  $A$  is hatched in the horizontal direction.
• The compliment  $\overline {B}$  of  $B$  is hatched in the vertical direction.
• The complement  $\overline{A \cup B}$  of the union  ${A \cup B}$  is hatched both horizontally and vertically.
• It is thus equal to the intersection  $\overline{A} \cap \overline{B}$  of the two complement sets of  $A$  and  $B$:
$$\overline{A \cup B} = \overline{A} \cap \overline{B}.$$

The second form of the de Morgan theorem can also be illustrated graphically with the same Venn diagram:

• The intersection  $A ∩ B$  $($shown in purple in the figure$)$  is neither horizontally nor vertically hatched.
• Accordingly, the complement  $\overline{A ∩ B}$  of the intersection is hatched either horizontally, vertically, or in both directions.
• By de Morgan's second theorem, the complement of the intersection equals the union of the two complementary sets of  $A$  and  $B$:
$$\overline{A \cap B} = \overline{A} \cup \overline{B}.$$

$\text{Example 5:}$  We consider again the experiment  "throwing a die".  The possible outcomes  (number of points)  are thus  $E_μ ∈ G = \{1, 2, 3, 4, 5, 6\}$.

We consider the two sets

• $A : =$  "The outcome is odd" $= \{1, 3, 5\}$,
• $B : =$  "The outcome is greater than  $2$" $= \{3, 4, 5, 6\}$.

From this follow the two complementary sets

• $\overline {A} : =$  "The outcome is even" $= \{2, 4, 6\}$,
• $\overline {B} : =$  "The outcome is smaller than  $3$" $= \{1, 2\}$.

Further,  using the above theorems,  we obtain the following sets:

$$\overline{A \cup B} = \overline{A} \cap \overline{B} = \{2\},$$
$$\overline{\it A \cap \it B} =\overline{\it A} \cup \overline{\it B} = \{1,2,4,6\}.$$

## Disjoint sets

$\text{Definition:}$  Two sets  $A$  and  $B$  are called  disjoint or  incompatible,

• if there is no single element,
• that is contained in both  $A$  and  $B$.

Disjoint sets in the Venn diagram

The diagram shows two disjoint sets  $A$  and  $B$  in the Venn diagram.

In this special case,  the following statements hold:

• The intersection of two disjoint sets  $A$  and  $B$  always yields the empty set:
$${\rm Pr}(A \cap B) = {\rm Pr}(\phi) = \rm 0.$$
• The probability of the union set of two disjoint sets  $A$  and  $B$  is always equal to the sum of the two individual probabilities:
$${\rm Pr}( A \cup B) = {\rm Pr}( A) + {\rm Pr}(B).$$

$\text{Example 6:}$  We consider again the experiment  "throwing a die".  The possible outcomes  (number of points)  are thus  $E_μ ∈ G = \{1, 2, 3, 4, 5, 6\}$.

In our standard experiment,  the two sets are now

• $A :=$  "The outcome is smaller than  $3$" $= \{1, 2\}$   ⇒   ${\rm Pr}( A) = 2/6$,
• $B :=$  "The outcome is greater than  $3$" $= \{4, 5,6\}$   ⇒   ${\rm Pr}( B) = 3/6$

disjoint to each other,  since  $A$  and  $B$  do not contain a single common element.

• The intersection yields the empty set:  ${A \cap B} = \phi$.
• The probability of the union set  ${A \cup B} = \{1, 2, 4, 5, 6\}$  is equal to  ${\rm Pr}( A) + {\rm Pr}(B) = 5/6.$

Only for disjoint sets  $A$  and  $B$,  the relation  ${\rm Pr}( A \cup B) = {\rm Pr}( A) + {\rm Pr}(B)$  holds for the probability of the union set.

But how is this probability calculated for general events that are not necessarily disjoint?

Consider the right-hand Venn diagram with the intersection  $A ∩ B$  shown in purple.

• The red set contains all the elements that belong to  $A$,  but not to  $B$.
• The elements of  $B$, that are not simultaneously contained in  $A$  are shown in blue.
• All red,  blue,  and purple surfaces together make up the union set  $A ∪ B$.

From this set-theoretic representation,  one can see the following relationships:

$${\rm Pr}(A) \hspace{0.8cm}= {\rm Pr}(A \cap B) + {\rm Pr}(A \cap \overline{B}),$$
$${\rm Pr}(B) \hspace{0.8cm}= {\rm Pr}(A \cap B) \rm +{\rm Pr}(\overline{A} \cap {B}),$$
$${\rm Pr}(A \cup B) ={\rm Pr}(A \cap B) +{\rm Pr} ({A} \cap \overline{B}) + {\rm Pr}(\overline{A} \cap {B}).$$

Adding the first two equations and subtracting from them the third,  we get:

$${\rm Pr}(A) +{\rm Pr}(B) -{\rm Pr}(A \cup B) = {\rm Pr}(A \cap B).$$

$\text{Definition:}$  By rearranging this equation, one arrives at the so-called  Addition Rule  for any two, not necessarily disjoint events:

$${\rm Pr}(A \cup B) = {\rm Pr}(A) + {\rm Pr}(B) - {\rm Pr}(A \cap B).$$

$\text{Example 7:}$  We consider again the experiment  "throwing a die".  The possible outcomes  (number of points)  are thus  $E_μ ∈ G = \{1, 2, 3, 4, 5, 6\}$.

We consider the two sets

• $A :=$  "The outcome is odd" $= \{1, 3, 5\}$   ⇒   ${\rm Pr}(A) = 3/6$,
• $B :=$  "The outcome is greater than  $2$" $= \{3, 4, 5, 6\}$   ⇒   ${\rm Pr}(B) = 4/6$.

This gives the following probabilities

• of the union   ⇒   ${\rm Pr}(A ∪ B) = 5/6$, and
• of the intersection   ⇒   ${\rm Pr}(A ∩ B) = 2/6$.

The numerical values show the validity of the addition theorem:   $5/6 = 3/6 + 4/6 − 2/6$.

## Complete system

In the last section to this chapter,  we consider again more than two possible events, namely, in general,  $I$.

• These events will be denoted by  $A_i$   ⇒   the running index $i$ can be in the range  $1 ≤ i ≤ I$.

$\text{Definition:}$  A constellation with events  $A_1, \hspace{0.1cm}\text{...}\hspace{0.1cm} , A_i, \hspace{0.1cm}\text{...}\hspace{0.1cm} , A_I$  is called a  complete system,  if and only if the following two conditions are satisfied:

(1)   All events are pairwise disjoint:

$$A_i \cap A_j = \it \phi \hspace{0.25cm}\rm for\hspace{0.15cm}all\hspace{0.25cm}\it i \ne j.$$

(2)   The union of all event sets gives the universal set:

$$\bigcup_{i=1}^{I} A_i = G.$$

Given these two assumptions, the sum of all probabilities is then:

$$\sum_{i =1}^{ I} {\rm Pr}(A_i) = 1.$$

$\text{Example 8:}$

• The sets  $A_1 := \{1, 5\}$  and  $A_2 := \{2, 3\}$  together with the set  $A_3 := \{4, 6\}$  result in a complete system in the random experiment  "throwing a die",
• but not in the experiment  "throwing a roulette ball".

$\text{Example 9:}$  Another example of a complete system is the discrete random variable  $X = \{ x_1, x_2, \hspace{0.1cm}\text{...}\hspace{0.1cm} , x_I\}$  with the likelihood corresponding to the following  probability mass function  $\rm (PMF)$:

$$P_X(X) = \big [ \hspace{0.1cm} P_X(x_1),\ P_X(x_2), \hspace{0.05cm}\text{...}\hspace{0.15cm},\ P_X(x_I) \hspace{0.05cm} \big ] = \big [ \hspace{0.1cm} p_1, p_2, \hspace{0.05cm}\text{...} \hspace{0.15cm}, p_I \hspace{0.05cm} \big ] \hspace{0.05cm}$$
$$\Rightarrow \hspace{0.3cm} p_1 = P_X(x_1) = {\rm Pr}(X=x_1) \hspace{0.05cm}, \hspace{0.2cm}p_2 = {\rm Pr}(X=x_2) \hspace{0.05cm},\hspace{0.05cm}\text{...}\hspace{0.15cm},\hspace{0.2cm} p_I = {\rm Pr}(X=x_I) \hspace{0.05cm}.$$
• The possible outcomes  $x_i$  of the random variable  $X$  are pairwise disjoint to each other.
• The sum of all likelihoods  $p_1 + p_2 + \hspace{0.1cm}\text{...}\hspace{0.1cm} + \hspace{0.05cm} p_I$  always yields the result  $1$.

$\text{Example 10:}$  Let  $X= \{0, 1, 2 \}$  and  $P_X (X) = \big[0.2, \ 0.5, \ 0.3\big]$. Then holds:

$${\rm Pr}(X=0) = 0.2 \hspace{0.05cm}, \hspace{0.2cm} {\rm Pr}(X=1) = 0.5 \hspace{0.05cm}, \hspace{0.2cm} {\rm Pr}(X=2) = 0.3 \hspace{0.05cm}.$$

With random variable  $X = \{1, \pi, {\rm e} \}$  and the same  $P_X(X) = \big[0.2, \ 0.5, \ 0.3\big]$  the assignments are:

$${\rm Pr}(X=1) = 0.2 \hspace{0.05cm}, \hspace{0.2cm} {\rm Pr}(X=\pi) = 0.5 \hspace{0.05cm}, \hspace{0.2cm} {\rm Pr}(X={\rm e}) = 0.3 \hspace{0.05cm}.$$

Hints:

• The  probability mass function  $P_X(X)$  only makes statements about the probabilities,  not about the set of values  $\{x_1, x_2, \hspace{0.1cm}\text{...}\hspace{0.1cm} , x_I\}$  of the random variable  $X$.
• This additional information is provided by the  probability density function  $\rm (PDF)$.