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

\[ \Omega = \{1, 2, 3, 4, 5, 6\}.\]

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 17 (Event space)

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

  1. \(\mathcal{F}\) is non-empty,

  2. if \(A \in \mathcal{F}\), then \(A^C = \Omega \setminus A \in \mathcal{F},\)

  3. if \(A_1, A_2, ... \in \mathcal{F}\) then \(\bigcup^\infty_{i = 1} A_i \in \mathcal{F}.\)

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 \(\Omega\).

  • Is closed under finite unions of its elements.

  • Is closed under countable intersections of its subsets.

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 18 (Probability measure)

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

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

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

  3. \(\mathbb{P}\) is countably additive, meaning that if \(A_1, A_2 ... \in \mathcal{F}\) are disjoint, then

\[\begin{equation}\mathbb{P}\left(\sum^\infty_{n = 1}A_n\right) = \sum^\infty_{n = 1}\mathbb{P}\left(A_n\right). \end{equation}\]

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 19 (Probability space)

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

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

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

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

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

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

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

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

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

  5. 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 20 (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

\[\mathbb{P}(A | B) = \frac{\mathbb{P}(A \cap B)}{\mathbb{P}(B)}.\]

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 meaningless. 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 21 (Independence)

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

\[ \mathbb{P}(A \cap B) = \mathbb{P}(A) \mathbb{P}(B).\]

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 7 (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.

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 8 (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

\[\mathbb{P}(A) = \sum_n \mathbb{P}(A | B_n)\mathbb{P}(B_n).\]

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 9 (Bayes’ theorem)

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

\[ \mathbb{P}(B_k | A) = \frac{\mathbb{P}(A | B_k)\mathbb{P}(B_k)}{\sum_n \mathbb{P}(A | B_n)\mathbb{P}(B_n)}.\]