The concept of random vector is a multidimensional generalization of the concept of random variable.
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Suppose that we conduct a probabilistic experiment and that the possible outcomes of the experiment are described by a sample space .
A random vector is a vector whose value depends on the outcome of the experiment, as stated by the following definition.
Definition Let be a sample space. A random vector is a function from the sample space to the set of -dimensional real vectors :
In rigorous probability theory, the function is also required to be measurable (a concept found in measure theory - see a more rigorous definition of random vector).
The real vector associated to a sample point is called a realization of the random vector.
The set of all possible realizations is called support and is denoted by .
Denote by the probability of an event . When dealing with random vectors, the following conventions are used:
If , we often write with the meaning
If , we sometimes use the notation with the meaningIn applied work, it is very commonplace to build statistical models where a random vector is defined by directly specifying (in which case the specification of the sample space is omitted altogether).
We often write instead of , that is, we omit the dependence on .
The following example shows how a random vector can be defined on a sample space.
Example Two coins are tossed. The possible outcomes of each toss can be either tail () or head (). The sample space isThe four possible outcomes are assigned equal probabilities:If tail () is the outcome, we win one dollar, if head () is the outcome we lose one dollar. A 2-dimensional random vector indicates the amount we win (or lose) on each toss:The probability of winning one dollar on both tosses isThe probability of losing one dollar on the second toss is
This section and the next one deal with discrete and continuous vectors, two kinds of random vectors that have special properties and are often found in applications.
Discrete vectors are defined as follows.
Definition A random vector is discrete if and only if
its support is a countable set;
there is a function , called the joint probability mass function (or joint pmf, or joint probability function) of , such that, for any :
The following notations are used interchangeably to indicate the joint probability mass function:
In the second and third notation the components of are explicitly indicated.
Example Suppose is a -dimensional random vector whose components ( and ) can take only two values: or . Furthermore, the four possible combinations of and are all equally likely. is an example of a discrete vector. Its support is Its probability mass function is
Continuous vectors are defined as follows.
The following notations are used interchangeably to indicate the joint probability density function:
In the second and third notation the components of the random vector are explicitly indicated.
Example Suppose is a -dimensional random vector whose components ( and ) are independent uniform random variables (on the interval ). Then, is an example of a continuous vector. Its support isIts joint probability density function isThe probability that the realization of falls in the rectangle is
Random vectors, also those that are neither discrete nor continuous, are often described using their joint distribution function.
Definition Let be a random vector. The joint distribution function (or joint df, or joint cumulative distribution function, or joint cdf) of is a function such thatwhere the components of and are denoted by and respectively, for .
The following notations are used interchangeably to indicate the joint distribution function:
In the second and third notation the components of the random vector are explicitly indicated.
Sometimes, we talk about the joint distribution of a random vector, without specifying whether we are referring to
the joint distribution function;
the joint pmf (in the case of discrete random vectors);
the joint pdf (in the case of continuous random vectors).
This ambiguity is legitimate, since
the joint pmf completely determines (and is completely determined by) the joint distribution function of a discrete vector;
the joint pdf completely determines (and is completely determined by) the joint distribution function of a continuous vector.
In the remainder of this lecture, we use the term joint distribution when we are making statements that apply both to the distribution function and to the probability mass (or density) function of a random vector.
The following subsections contain more details about random vectors.
A random matrix is a matrix whose entries are random variables.
It is not necessary to develop a separate theory for random matrices because a random matrix can always be written as a random vector.
Given a random matrix , its vectorization, denoted by , is the random vector obtained by stacking the columns of on top of each other.
Example Let be the following random matrix:The vectorization of is the following random vector:
When is a discrete vector, then we say that is a discrete random matrix and the joint pmf of is just the joint pmf of .
By the same token, when is a continuous vector, then we say that is a continuous random matrix and the joint pdf of is just the joint pdf of .
Let be the -th component of a -dimensional random vector .
The distribution function of is called marginal distribution function of .
If is discrete, then is a discrete random variable and its probability mass function is called marginal probability mass function of .
If is continuous, then is a continuous random variable and its probability density function is called marginal probability density function of .
The process of deriving the distribution of a component of a random vector from the joint distribution of is known as marginalization.
Marginalization can also have a broader meaning: it can refer to the act of deriving the joint distribution of a subset of the set of components of from the joint distribution of .
For example, if is a random vector having three components (, and ), we can marginalize the joint distribution of , and to find the joint distribution of and (in this case we say that is marginalized out of the joint distribution of , and ).
Let be the -th component of a -dimensional discrete random vector . The marginal probability mass function of can be derived from the joint probability mass function of as follows:where the sum is over the set
In other words, the probability that is obtained as the sum of the probabilities of all the vectors in such that their -th component is equal to .
Let be the -th component of a discrete random vector . By marginalizing out of the joint distribution of , we obtain the joint distribution of the remaining components of , that is, we obtain the joint distribution of the random vector defined as follows:
The joint probability mass function of is computed as follows:where the sum is over the set
In other words, the joint probability mass function of can be computed by summing the joint probability mass function of over all values of that belong to the support of .
Let be the -th component of a -dimensional continuous random vector . The marginal probability density function of can be derived from the joint probability density function of as follows:
In other words, the joint probability density function, evaluated at , is integrated with respect to all variables except (so it is integrated a total of times).
Let be the -th component of a continuous random vector . By marginalizing out of the joint distribution of , we obtain the joint distribution of the remaining components of , that is, we get the joint distribution of the random vector defined as follows:
The joint probability density function of is computed as follows:
In other words, the joint probability density function of can be computed by integrating the joint probability density function of with respect to .
Note that, if is continuous, then
Hence, by taking the -th order cross-partial derivative with respect to of both sides of the above equation, we obtain
We report here a more rigorous definition of random vector by using the formalism of measure theory. This definition is analogous to the measure-theoretic definition given in the lecture on random variables, to which you should refer for a more detailed explanation.
Definition Let be a probability space. Let be the Borel sigma-algebra of (i.e., the smallest sigma-algebra containing all open hyper-rectangles in ). A function such that for any is said to be a random vector on .
This definition ensures that the probability that the realization of the random vector will belong to a set can be defined as because the set belongs to the sigma-algebra and, as a consequence, its probability is well-defined.
Some solved exercises on random vectors can be found below.
Let be a discrete random vector and denote its components by and .
Let the support of be the set of all vectors such that their entries belong to the set of the first three natural numbers, that is, where
Let the joint probability mass function of be
Find .
Trivially, we need to evaluate the joint probability mass function at the point , that is,
Let be a discrete random vector and denote its components by and .
Let the support of be the set of all vectors such that their entries belong to the set of the first three natural numbers, that is,where
Let the joint probability mass function of be
Find .
There are only two possible cases that give rise to the occurrence . These cases areandTherefore, since these two cases are disjoint events, we can use the additivity of probability:
Let be a discrete random vector and denote its components by and .
Let the support of beand its joint probability mass function be
Derive the marginal probability mass functions of and .
The support of isWe need to compute the probability of each element of the support of :Thus, the probability mass function of isThe support of isWe need to compute the probability of each element of the support of :Thus, the probability mass function of is
Let be a continuous random vector and denote its components by and .
Let the support of be that is, the set of all vectors such that the first component belongs to the interval and the second component belongs to the interval .
Let the joint probability density function of be
Compute .
By the very definition of joint probability density function:
Let be a continuous random vector and denote its components by and .
Let the support of be that is, the set of all vectors such that the first component belongs to the interval and the second component belongs to the interval .
Let the joint probability density function of be
Compute .
First of all note that if and only if . By using the definition of joint probability density function, we obtainNow, note that, when , the inner integral isTherefore,
Let be a continuous random vector and denote its components by and .
Let the support of be (i.e., the set of all -dimensional vectors with positive entries) and its joint probability density function be
Derive the marginal probability density functions of and .
The support of is(recall that and ). We can find the marginal density by integrating the joint density with respect to :When , then and the above integral is trivially equal to . Thus, when , then . When , thenbut the first of the two integrals is zero since when ; as a consequence,So, by putting pieces together, we get the marginal density function of :By symmetry, the marginal density function of is
Please cite as:
Taboga, Marco (2021). "Random vectors", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix. https://www.statlect.com/fundamentals-of-probability/random-vectors.
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