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Conditional Distribution of Y Given X Continuous

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A conditional distribution is the probability distribution of a random variable, calculated according to the rules of conditional probability after observing the realization of another random variable.

Table of Contents

Table of contents

  1. Overview

  2. Conditioning on events

  3. Discrete random vectors

  4. Continuous random vectors

  5. The general case

  6. More details

    1. Conditional distribution of a random vector

    2. The joint distribution as a product of marginal and conditional

  7. Solved exercises

    1. Exercise 1

    2. Exercise 2

    3. Exercise 3

We will discuss how to update the probability distribution of a random variable X after receiving the information that another random variable Y has taken a specific value $y$ .

The updated probability distribution of X will be called the conditional distribution of X given Y=y .

The two random variables X and Y , considered together, form a random vector [eq1] .

Depending on the characteristics of the random vector [eq2] , different procedures need to be followed in order to compute the conditional probability distribution of X given Y=y .

In the remainder of this lecture, these procedures are presented in the following order:

  1. first, we tackle the case in which the random vector [eq3] is a discrete random vector;

  2. then, we tackle the case in which [eq4] is a continuous random vector;

  3. finally, we briefly discuss the case in which [eq2] is neither discrete nor continuous.

Note that if we are able to update the probability distribution of X when we receive the information that Y=y , then we can also revise the distribution of X when we get to know that a generic event E has happened.

It suffices to set $Y=1_{E}$ , where $1_{E}$ is the indicator function of the event E , and compute the distribution of X conditional on the realization $Y=1_{E}=1$ .

In the case in which [eq4] is a discrete random vector, the probability mass function (pmf) of X conditional on the information that Y=y is called conditional probability mass function.

Definition Let [eq7] be a discrete random vector. We say that a function [eq8] is the conditional probability mass function of X given Y=y if, for any $xin U{211d} $ , [eq9] where [eq10] is the conditional probability that $X=x$ given that Y=y .

How do we derive the conditional pmf from the joint pmf [eq11] ?

The following proposition provides an answer to this question.

Proposition Let [eq12] be a discrete random vector. Let [eq13] be its joint pmf, and [eq14] the marginal pmf of Y . The conditional pmf of X given Y=y is [eq15] provided [eq16] .

Proof

This is just the usual formula for computing conditional probabilities (conditional probability equals joint probability divided by marginal probability): [eq17]

In the proposition above, we assume that the marginal pmf [eq14] is known. If it is not, it can be derived from the joint pmf [eq19] by marginalization.

Example Let the support of [eq20] be [eq21] and its joint pmf be [eq22] Let us compute the conditional pmf of X given $Y=0$ . The support of Y is [eq23] The marginal pmf of Y evaluated at $y=0$ is [eq24] The support of X is [eq25] Thus, the conditional pmf of X given $Y=0$ is [eq26]

When [eq27] , it is in general not possible to unambiguously derive the conditional pmf of X , as we show below with an example.

This impossibility (known as the Borel-Kolmogorov paradox) is not particularly worrying, as it is seldom relevant in applications.

Example The example is a bit involved. You might safely skip it on a first reading. Suppose that the sample space Omega is the set of all real numbers between 0 and 1 : [eq28] It is possible to build a probability measure $QTR{rm}{P}$ on Omega , such that $QTR{rm}{P}$ assigns to each sub-interval of $left[ 0,1 ight] $ a probability equal to its length, that is, [eq29] This is the same sample space discussed in the lecture on zero-probability events. Define a random variable X as follows: [eq30] and another random variable Y as follows: [eq31] Both X and Y are discrete random variables and, considered together, they constitute a discrete random vector [eq32] . Suppose that we want to compute the conditional pmf of X conditional on $Y=1$ . It is easy to see that [eq33] . As a consequence, we cannot use the formula [eq34] because division by zero is not possible. Also the technique of deriving a conditional probability implicitly, as a realization of a conditional probability with respect to a sigma-algebra does not allow us to unambiguously derive [eq35] . In this case, the partition of interest is [eq36] , where [eq37] and [eq38] can be viewed as the realization of the conditional probability [eq39] when $omega in G_{1}$ . The fundamental property of conditional probability [eq40] is satisfied in this case if and only if, for a given x , the following system of equations is satisfied: [eq41] which implies [eq42] The second equation does not help to determine [eq43] . So, from the first equation, it is evident that [eq44] is undetermined (any number, when multiplied by zero, gives zero). One can show that also the requirement that [eq45] be a regular conditional probability does not help to pin down [eq38] . What does it mean that [eq38] is undetermined? It means that any choice of [eq35] is legitimate, provided the requirement [eq49] is satisfied. Is this really a paradox? No, because conditional probability with respect to a partition is defined up to almost sure equality, and $G_{1} $ is a zero-probability event. As a consequence, the value that [eq50] takes on $G_{1}$ does not matter. Roughly speaking, we do not really need to care about zero-probability events, provided there is only a countable number of them.

In the case in which [eq4] is a continuous random vector, the probability density function (pdf) of X conditional on the information that Y=y is called conditional probability density function.

Definition Let [eq52] be a continuous random vector. We say that a function [eq53] is the conditional probability density function of X given Y=y if, for any interval [eq54] , [eq55] and [eq56] is such that the above integral is well defined.

How do we derive the conditional pdf from the joint pdf [eq57] ?

The following proposition provides an answer to this question.

Proposition Let [eq58] be a continuous random vector. Let [eq57] be its joint pdf, and [eq60] be the marginal pdf of Y . The conditional pdf of X given Y=y is [eq61] provided [eq62] .

Proof

Deriving the conditional distribution of X given Y=y is far from obvious. As explained in the lecture on random variables, whatever value of $y$ we choose, we are conditioning on a zero-probability event: [eq63] Therefore, the standard formula (conditional probability equals joint probability divided by marginal probability) cannot be used. However, it turns out that the definition of conditional probability with respect to a partition can be fruitfully applied in this case to derive the conditional pdf of X given Y=y . In order to prove that [eq64] is a legitimate choice, we need to prove that conditional probabilities calculated by using this conditional pdf satisfy the fundamental property of conditional probability: [eq65] for any H and E . Thanks to some basic results in measure theory, we can confine our attention to the events H and E that can be written as follows: [eq66] For these events, it is immediate to verify that the fundamental property of conditional probability holds. First, by the very definition of a conditional pdf, we have that [eq67] Furthermore, the indicator function [eq68] is also a function of Y . Therefore, the product [eq69] is a function of Y , and we can use the transformation theorem to compute its expected value: [eq70] The last equality proves the proposition.

Example Let the support of [eq20] be [eq72] and its joint pdf be [eq73] Let us compute the conditional pdf of X given $Y=1$ . The support of Y is [eq74] When [eq75] , the marginal pdf of Y is 0 ; when [eq76] , the marginal pdf is [eq77] Thus, the marginal pdf of Y is [eq78] When evaluated at $y=1$ , it is [eq79] The support of X is [eq80] Thus, the conditional pdf of X given $Y=1$ is [eq81]

In general, when [eq4] is neither discrete nor continuous, we can characterize the distribution function of X conditional on the information that Y=y .

Definition We say that a function [eq83] is the conditional distribution function of X given Y=y if and only if [eq84] where [eq85] is the conditional probability that $Xleq x$ given that Y=y .

There is no immediate way of deriving the conditional distribution of X given Y=y . However, we can characterize it by using the concept of conditional probability with respect to a partition, as follows.

Define the events $G_{y}$ as follows: [eq86] and a partition G of events as [eq87] where, as usual, $R_{Y}$ is the support of Y .

Then, for any $omega in G_{y}$ we have [eq88] where [eq89] is the probability that $Xleq x$ conditional on the partition G .

As we know, [eq89] is guaranteed to exist and is unique up to almost sure equality. Of course, this does not mean that we are able to compute it.

Nonetheless, this characterization is extremely useful because it allows us to speak of the conditional distribution of X given Y=y in general, without the need to specify whether X and Y are discrete or continuous.

The following sections contain more details about conditional distributions.

Conditional distribution of a random vector

We have discussed how to update the probability distribution of a random variable X after observing the realization of another random variable Y , that is, after receiving the information that Y=y .

What happens when X and Y are random vectors rather than random variables?

Basically, everything we said above still applies with straightforward modifications.

Thus, if X and Y are discrete random vectors, then the conditional probability mass function of X given Y=y is [eq91] provided [eq92] .

If X and Y are continuous random vectors then the conditional probability density function of X given Y=y is [eq93] provided [eq94] .

In general, the conditional distribution function of X given Y=y is [eq95]

The joint distribution as a product of marginal and conditional

As we have explained above, the joint distribution of X and Y can be used to derive the marginal distribution of Y and the conditional distribution of X given Y=y .

This process can also go in the reverse direction: if we know the marginal distribution of Y and the conditional distribution of X given Y=y , then we can derive the joint distribution of X and Y .

For discrete random variables, we have that [eq96]

For continuous random variables, we have that [eq97]

Below you can find some exercises with explained solutions.

Exercise 1

Let [eq4] be a discrete random vector with support [eq99] and joint probability mass function [eq100]

Compute the conditional probability mass function of X given $Y=0$ .

Solution

The marginal probability mass function of Y evaluated at $y=0$ is [eq101] The support of X is [eq102] Thus, the conditional probability mass function of X given $Y=0$ is [eq103]

Exercise 2

Let [eq4] be a continuous random vector with support [eq105] and its joint probability density function be [eq106]

Compute the conditional probability density function of X given $Y=2$ .

Solution

The support of Y is [eq107] When [eq108] , the marginal probability density function of Y is [eq109] ; when [eq110] , the marginal probability density function of Y is [eq111] Thus, the marginal probability density function of Y is [eq112] When evaluated at the point $y=2$ , it becomes [eq113] The support of X is [eq114] Thus, the conditional probability density function of X given $Y=2$ is [eq115]

Exercise 3

Let X be a continuous random variable with support [eq116] and probability density function [eq117]

Let Y be another continuous random variable with support [eq118] and conditional probability density function [eq119]

Find the marginal probability density function of Y .

Solution

Please cite as:

Taboga, Marco (2021). "Conditional probability distribution", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix. https://www.statlect.com/fundamentals-of-probability/conditional-probability-distributions.

brettyoult1948.blogspot.com

Source: https://www.statlect.com/fundamentals-of-probability/conditional-probability-distributions

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