# Events and Probabilities¶

This first chapter presents the fundamental definitions of sample and event spaces, probability measures and probability spaces. These enable a calculus of the probability of experiment outcomes. Fundamental results such as Bayes’ rule and the continuity of probability measures are presented.

## Sample and event spaces¶

We are interested in reasoning about the outcomes of an experiment \(\mathcal{E}\). We denote the set of possible outcomes of \(\mathcal{E}\) by \(\Omega\) and we call this the **event space**. The members \(\omega \in \Omega\) are called **elementary events**. For example, if \(\mathcal{E}\) is an experiment where a die is tossed once, we could define the elementary events to be the possible outcomes of the toss and \(\Omega\) to be

As their name suggests, elementary events represent experimental outcomes which are in some sense atomic. Using sets of these elementary outcomes we can express more complicated outcomes which might be of interest. For example, the event where the outcome of the toss is even corresponds to the set of elementary events \(\{2, 4, 6\}\). Experimental outcomes are naturally represented as sets of elementary events, that is in terms of subsets of \(\Omega\). In addition to the sample space \(\Omega\), we define **event spaces** to represent the events of interest. These event spaces are defined to have certain properties which enable reasoning about probabilities of unions, intersections and complements of events.

**Definition (Event space)** The collection \(\mathcal{F}\) of subsets of the sample space \(\Omega\) is called an event space if

Note that an event space is always defined with respect to a sample space, and a sample space can have more than one event spaces defined on it. Three consequences of the above definition are that any event space \(\mathcal{F}\):

Contains the empty set \(\emptyset\) and the whole set \(\Omega\). Since \(\mathcal{F}\) is non-empty, it contains at least one set \(A\), and by definition also \(A^C\), as well as \(A \cup A^C = \Omega\). Thus \(\Omega \in \mathcal{F}\) and also \(\emptyset = \Omega^C \in \mathcal{F}\).

Is closed under

*finite*unions of its elements. We can write any finite union of the form \(A_1 \cup A_2 ... A_n\) as a countably infinite union \(B_1 \cup B_2 ...\), where \(B_i = A_i\) for \(i \leq n\) and \(B_i = \emptyset\) otherwise, proving that \(A_1 \cup A_2 ... A_n \in \mathcal{F}\).Is closed under countable intersections of its subsets. We can write the intersection of \(A, B \in \mathcal{F}\) as \(A \cap B = \Omega \setminus (A \cap B)^C\) and since \((A \cap B)^C = A^C \cup B^C \in \mathcal{F}\), then \(A \cap B \in \mathcal{F}\).

## Probability measures¶

We have defined the sample space \(\Omega\) and the event space \(\mathcal{F}\) of the experiment, but we are still missing the probabilities of the experimental outcomes. This is achieved by a mapping called the **probability measure** which assigns a probability to each event in \(\mathcal{F}\).

**Definition (Probability measure)** A mapping \(\mathbb{P} : \mathcal{F} \to \mathbb{R}\) is called a **probability measure** on \((\Omega, \mathcal{F})\) if

\(\mathbb{P}(A) \geq 0\) for \(A \in \mathcal{F}\).

\(\mathbb{P}(\Omega) = 1\).

it is

**countably additive**in that if \(A_1, A_2 ... \in \mathcal{F}\) are disjoint, then \[\mathbb{P}\left(\sum^\infty_{n = 1}A_n\right) = \sum^\infty_{n = 1}\mathbb{P}\left(A_n\right).\]

Using conditions (1) and (2) above we can also show that \(\mathbb{P}(\emptyset) = 0\). From this and condition (3) we can also show that probability measures are also **finitely additive**.

## Probability spaces¶

Sample spaces, event spaces and probability measures can be combined into a probability space associated with our experiment.

**Definition (Probability space)** A **probability space** is a triplet \((\Omega, \mathcal{F}, \mathbb{P})\) of objects such that

\(\Omega\) is a non-empty set.

\(\mathcal{F}\) is an event space on \(\Omega\).

\(\mathbb{P}\) is a probability measure on \((\Omega, \mathcal{F})\).

From the definitions of \(\Omega\), \(\mathcal{F}\) and \(\mathbb{P}\) follow several basic facts:

If \(A, B \in \mathcal{F}\), then \(A \setminus B \in \mathcal{F}\).

If \(A_1, A_2, ... \in \mathcal{F}\), then \(\cap^\infty_{n = 1}A_n \in \mathcal{F}\).

If \(A \in \mathcal{F}\) then \(\mathbb{P}(A) + \mathbb{P}(A^C) = 1\).

If \(A, B \in \mathcal{F}\) then \(\mathbb{P}(A \cup B) + \mathbb{P}(A \cap B) = \mathbb{P}(A) + \mathbb{P}(B)\).

If \(A, B \in \mathcal{F}\) and \(A \subseteq B\) then \(\mathbb{P}(A) \leq \mathbb{P}(B)\).

## Conditional probability and independence¶

Often we may have partial information about the outcome of an experiment, and want to adjust our beliefs about the outcome based on these beliefs. We are therefore interested in the probability of some event \(A\) occuring, given that another event \(B\) occurs. This updated probability is called a conditional probability.

**Definition (Conditional probabiility)** Given \(A, B \in \mathcal{F}\) and \(\mathbb{P}(B) > 0\), the **conditional probability** of \(A\) given \(B\), \(\mathbb{P}(A | B)\), is defined as

The condition \(\mathbb{P}(B) > 0\) is in place to ensure that the division is defined, or equivalently that \(\mathbb{P}(A | B)\) is a sensible quantity: if \(\mathbb{P}(B)= 0\) then \(B\) would never occur so the statement \(A | B\) is senseless. The definition of the conditional probability is often referred to as the **product rule**, while the finite additivity of \(\mathbb{P}\) defined earlier, is often referred to as the **sum rule**.

In some cases, information coming from one event might not give us any information about another event, in the sense that the probability of \(A | B\) is equal to the probability of \(A\), in which case we say \(A\) and \(B\) are **conditionally independent**.

**Definition (Independence)** Events \(A, B \in \mathcal{F}\) are called **independent** if

This definition of independence is slightly more general than the statement “\(A, B\) are independent \(\iff\) \(\mathbb{P}(A | B) = \mathbb{P}(A)\)” in the sense that it allows for \(\mathbb{P}(B) = 0\). Conditional probabilities define valid probability spaces too, in the sense of the following result.

**Theorem (Conditional probability space)** If \((\Omega, \mathcal{F}, \mathbb{P})\) is a probability space and \(B \in \mathcal{F}\) with \(\mathbb{P} > 0\), then \((\Omega, \mathcal{F}, \mathbb{Q})\) where \(\mathbb{Q} : \mathcal{F} \to \mathbb{R}\) and \(\mathbb{Q}(A) = \mathbb{P}(A | B)\) is also a probability space.

This can be be proved by showing that \(\mathbb{Q}\) satisfies the three conditions of probability measures.

## The partition theorem and Bayes’ rule¶

Often, calculating probabilities of interest is made easier by applying the partition theorem shown below. This follows from the definition of conditional probability and the additivity of probability measures.

**Theorem (Partition theorem)** If \(B_1, B_2, ...\) is a partition of \(\Omega\), in the sense that the \(B_n\) are all disjoint and their union is \(\Omega\), then

The partition theorem is closely related to Bayes’ rule, which provides the way for computing the probability of \(B | A\) given the probability of \(A | B\). We are often interested in making statements about the probability of an event \(B\), given the fact that we have observed another event \(A\), starting from an experssion for

**Theorem (Bayes’ theorem)** If \(B_1, B_2, ...\) is a partition of \(\Omega\) with \(\mathbb{P}(B_n) > 0\), we have

## Continuity¶

One final important result, useful in calclations pertaining to the convergence of limits of probabilities, is the following **continuity theorem**.

**Theorem (Continuity theorem)** Let \((\Omega, \mathcal{F}, \mathbb{P})\)
be a probability space. If \(A_1 \subseteq A_2 \subseteq ...\) is an
nondecreasing sequence of sets with limit \(A\), then

The continuity theorem can also be written in the form of the limit of nonincreasing sets \(B_n \supseteq B_{n+1} \supseteq ... \to B\), in the sense