# C Probability

STAT 432 is not a course about probability. STAT 432 is a course that uses probability.

We give a very brief review of some necessary probability concepts. As the treatment is less than complete, a list of references is given at the end of the chapter. For example, we ignore the usual recap of basic set theory and omit proofs and examples. Reading the information below will likely be unsatisfying. Instead, we suggest that you skip it, engage with the relevant quizzes, then return as needed for reference.

## C.2 Probability Models

When discussing probability models, we speak of random experiments that produce one of a number of possible outcomes.

A probability model that describes the uncertainty of an experiment consists of two elements:

• The sample space, often denoted as $$\Omega$$, which is a set that contains all possible outcomes.
• A probability function that assigns to an event $$A$$ a nonnegative number, $$P[A]$$, that represents how likely it is that event $$A$$ occurs as a result of the experiment.

We call $$P[A]$$ the probability of event $$A$$. An event $$A$$ could be any subset of the sample space, not necessarily a single possible outcome. The probability law must follow a number of rules, which are the result of a set of axioms that we introduce now.

## C.3 Probability Axioms

Given a sample space $$\Omega$$ for a particular experiment, the probability function associated with the experiment must satisfy the following axioms.

1. Nonnegativity: $$P[A] \geq 0$$ for any event $$A \subset \Omega$$.
2. Normalization: $$P[\Omega] = 1$$. That is, the probability of the entire space is 1.
3. Additivity: For mutually exclusive events $$E_1, E_2, \ldots$$

$P\left[\bigcup_{i = 1}^{\infty} E_i\right] = \sum_{i = 1}^{\infty} P[E_i]$

Using these axioms, many additional probability rules can easily be derived.

## C.4 Probability Rules

Given an event $$A$$, and its complement, $$A^c$$, that is, the outcomes in $$\Omega$$ which are not in $$A$$, we have the complement rule:

$P[A^c] = 1 - P[A]$

In general, for two events $$A$$ and $$B$$, we have the addition rule:

$P[A \cup B] = P[A] + P[B] - P[A \cap B]$

If $$A$$ and $$B$$ are also disjoint, then we have:

$P[A \cup B] = P[A] + P[B]$

If we have $$n$$ mutually exclusive events, $$E_1, E_2, \ldots E_n$$, then we have:

$P\left[\textstyle\bigcup_{i = 1}^{n} E_i\right] = \sum_{i = 1}^{n} P[E_i]$

Often, we would like to understand the probability of an event $$A$$, given some information about the outcome of event $$B$$. In that case, we have the conditional probability rule provided $$P[B] > 0$$.

$P[A \mid B] = \frac{P[A \cap B]}{P[B]}$

Rearranging the conditional probability rule, we obtain the multiplication rule:

$P[A \cap B] = P[B] \cdot P[A \mid B] \cdot$

For a number of events $$E_1, E_2, \ldots E_n$$, the multiplication rule can be expanded into the chain rule:

$P\left[\textstyle\bigcap_{i = 1}^{n} E_i\right] = P[E_1] \cdot P[E_2 \mid E_1] \cdot P[E_3 \mid E_1 \cap E_2] \cdots P\left[E_n \mid \textstyle\bigcap_{i = 1}^{n - 1} E_i\right]$

Define a partition of a sample space $$\Omega$$ to be a set of disjoint events $$A_1, A_2, \ldots, A_n$$ whose union is the sample space $$\Omega$$. That is

$A_i \cap A_j = \emptyset$

for all $$i \neq j$$, and

$\bigcup_{i = 1}^{n} A_i = \Omega.$

Now, let $$A_1, A_2, \ldots, A_n$$ form a partition of the sample space where $$P[A_i] > 0$$ for all $$i$$. Then for any event $$B$$ with $$P[B] > 0$$ we have Bayes’ Theorem:

$P[A_i | B] = \frac{P[A_i]P[B | A_i]}{P[B]} = \frac{P[A_i]P[B | A_i]}{\sum_{i = 1}^{n}P[A_i]P[B | A_i]}$

The denominator of the latter equality is often called the law of total probability:

$P[B] = \sum_{i = 1}^{n}P[A_i]P[B | A_i]$

Note: When working with Bayes’ Theorem it is often useful to draw a tree diagram.

Two events $$A$$ and $$B$$ are said to be independent if they satisfy

$P[A \cap B] = P[A] \cdot P[B]$

This becomes the new multiplication rule for independent events.

A collection of events $$E_1, E_2, \ldots E_n$$ is said to be independent if

$P\left[\bigcap_{i \in S} E_i \right] = \prod_{i \in S}P[E_i]$

for every subset $$S$$ of $$\{1, 2, \ldots n\}$$.

If this is the case, then the chain rule is greatly simplified to:

$P\left[\textstyle\bigcap_{i = 1}^{n} E_i\right] = \prod_{i=1}^{n}P[E_i]$

## C.5 Random Variables

A random variable is simply a function which maps outcomes in the sample space to real numbers.

### C.5.1 Distributions

We often talk about the distribution of a random variable, which can be thought of as:

$\text{distribution} = \text{list of possible} \textbf{ values} + \text{associated} \textbf{ probabilities}$

This is not a strict mathematical definition, but is useful for conveying the idea.

If the possible values of a random variables are discrete, it is called a discrete random variable. If the possible values of a random variables are continuous, it is called a continuous random variable.

### C.5.2 Discrete Random Variables

The distribution of a discrete random variable $$X$$ is most often specified by a list of possible values and a probability mass function, $$p(x)$$. The mass function directly gives probabilities, that is,

$p(x) = p_X(x) = P[X = x].$

Note we almost always drop the subscript from the more correct $$p_X(x)$$ and simply refer to $$p(x)$$. The relevant random variable is discerned from context

The most common example of a discrete random variable is a binomial random variable. The mass function of a binomial random variable $$X$$, is given by

$p(x | n, p) = {n \choose x} p^x(1 - p)^{n - x}, \ \ \ x = 0, 1, \ldots, n, \ n \in \mathbb{N}, \ 0 < p < 1.$

This line conveys a large amount of information.

• The function $$p(x | n, p)$$ is the mass function. It is a function of $$x$$, the possible values of the random variable $$X$$. It is conditional on the parameters $$n$$ and $$p$$. Different values of these parameters specify different binomial distributions.
• $$x = 0, 1, \ldots, n$$ indicates the sample space, that is, the possible values of the random variable.
• $$n \in \mathbb{N}$$ and $$0 < p < 1$$ specify the parameter spaces. These are the possible values of the parameters that give a valid binomial distribution.

Often all of this information is simply encoded by writing

$X \sim \text{bin}(n, p).$

### C.5.3 Continuous Random Variables

The distribution of a continuous random variable $$X$$ is most often specified by a set of possible values and a probability density function, $$f(x)$$. (A cumulative density or moment generating function would also suffice.)

The probability of the event $$a < X < b$$ is calculated as

$P[a < X < b] = \int_{a}^{b} f(x)dx.$

Note that densities are not probabilities.

The most common example of a continuous random variable is a normal random variable. The density of a normal random variable $$X$$, is given by

$f(x | \mu, \sigma^2) = \frac{1}{\sigma\sqrt{2\pi}} \cdot \exp\left[\frac{-1}{2} \left(\frac{x - \mu}{\sigma}\right)^2 \right], \ \ \ -\infty < x < \infty, \ -\infty < \mu < \infty, \ \sigma > 0.$

• The function $$f(x | \mu, \sigma^2)$$ is the density function. It is a function of $$x$$, the possible values of the random variable $$X$$. It is conditional on the parameters $$\mu$$ and $$\sigma^2$$. Different values of these parameters specify different normal distributions.
• $$-\infty < x < \infty$$ indicates the sample space. In this case, the random variable may take any value on the real line.
• $$-\infty < \mu < \infty$$ and $$\sigma > 0$$ specify the parameter space. These are the possible values of the parameters that give a valid normal distribution.

Often all of this information is simply encoded by writing

$X \sim N(\mu, \sigma^2)$

### C.5.4 Distributions in R

R is an excellent, if not best, tool for performing probability distribution calculations. For a large number of distributions, it has four built in functions:

• d*(x, ...) returns the PDF at $$x$$ (for continuous distributions) or the PMG at $$x$$ (for discrete distributions)
• p*(q, ...) returns the CDF at quantile $$q$$, that is $$P[X \leq q]$$
• q*(p, ...) returns $$c$$ such that $$P[x \leq c] = p$$
• r*(n, ...) returns $$n$$ randomly generated observations

The * can be any of the disributions built in to R. The ... represents additional arguments, including the parameters of the various distributions.

### C.5.5 Several Random Variables

Consider two random variables $$X$$ and $$Y$$. We say they are independent if

$f(x, y) = f(x) \cdot f(y)$

for all $$x$$ and $$y$$. Here $$f(x, y)$$ is the joint density (mass) function of $$X$$ and $$Y$$. We call $$f(x)$$ the marginal density (mass) function of $$X$$. Then $$f(y)$$ the marginal density (mass) function of $$Y$$. The joint density (mass) function $$f(x, y)$$ together with the possible $$(x, y)$$ values specify the joint distribution of $$X$$ and $$Y$$.

Similar notions exist for more than two variables.

## C.6 Expectations

For discrete random variables, we define the expectation of the function of a random variable $$X$$ as follows.

$\mathbb{E}[g(X)] \triangleq \sum_{x} g(x)p(x)$

For continuous random variables we have a similar definition.

$\mathbb{E}[g(X)] \triangleq \int g(x)f(x) dx$

For specific functions $$g$$, expectations are given names.

The mean of a random variable $$X$$ is given by

$\mu_{X} = \text{mean}[X] \triangleq \mathbb{E}[X].$

So for a discrete random variable, we would have

$\text{mean}[X] = \sum_{x} x \cdot p(x)$

For a continuous random variable we would simply replace the sum by an integral.

The variance of a random variable $$X$$ is given by

$\sigma^2_{X} = \text{var}[X] \triangleq \mathbb{E}[(X - \mathbb{E}[X])^2] = \mathbb{E}[X^2] - (\mathbb{E}[X])^2.$

The standard deviation of a random variable $$X$$ is given by

$\sigma_{X} = \text{sd}[X] \triangleq \sqrt{\sigma^2_{X}} = \sqrt{\text{var}[X]}.$

The covariance of random variables $$X$$ and $$Y$$ is given by

$\text{cov}[X, Y] \triangleq \mathbb{E}[(X - \mathbb{E}[X])(Y - \mathbb{E}[Y])] = \mathbb{E}[XY] - \mathbb{E}[X] \cdot \mathbb{E}[Y].$

## C.7 Likelihood

Consider $$n$$ iid random variables $$X_1, X_2, \ldots X_n$$. We can then write their likelihood as

$\mathcal{L}(\theta \mid x_1, x_2, \ldots x_n) \triangleq f(x_1, x_2, \ldots, x_n; \theta) = \prod_{i = 1}^n f(x_i; \theta)$

where $$f(x_1, x_2, \ldots, x_n; \theta)$$ is the joint density (or mass) of $$X_1, X_2, \ldots X_n$$ and $$f(x_i; \theta)$$ is the density (or mass) function of random variable $$X_i$$ evaluated at $$x_i$$ with parameter $$\theta$$. (Note: The last equality above only holds for iid random variables.)

Whereas a probability or density is a function of a possible observed value given a particular parameter value, a likelihood is the opposite. It is a function of a possible parameter values given observed data. Likelihoods are calculated when the data (the $$x_i$$) are known and the parameters ($$\theta$$) are unknown. That is, a likelihood and a joint density will “look” the same, that is contain the same symbols. The meaning of these symbols change depending on what is known. If the data is known, and the parameters is unknown, you have a likelihood. If the parameters are known, and the data are unknown, you have a joint density. The definition above is an acknowledgement of this. The likelihood is defined to be the joint density when the data are known but the parameter(s) is unknown.

Maximizing a likelihood is a common technique for fitting a model to data, however, most often we maximum the log-likelihood, as the likelihood and log-likelihood obtain their maximum at the same point.

$\log \mathcal{L}(\theta \mid x_1, x_2, \ldots x_n) = \sum_{i = 1}^{n} \log f(x_i; \theta)$

As an example, suppose that the data vector x_data contains observations from a random sample $$X_1, X_2, \ldots, X_n$$ that is assumed to be sampled from a Poisson distribution with (unknown) parameter $$\lambda$$.

set.seed(42)
x_data = rpois(n = 25, lambda = 6) # generating data (assume this is not known)
head(x_data) # check data
##   9 10  5  8  7  6

We can use R to calculate the likelihood for various possible values of $$\lambda$$ given this data.

# calculate the likelihood when lambda = 5
prod(dpois(x = x_data, lambda = 5)) 
##  2.609375e-30

The above code takes advantage of the vectorized nature of the dpois() function. Often, especially for computational reasons, we prefer to directly obtain the log-likelihood.

# calculate the log-likelihood when lambda = 5
sum(log(dpois(x = x_data, lambda = 5))) 
##  -68.11844

To understand why this is necessary, repeat the above, but with a much larger sample size. Also note that the d*() functions in R have an option to return logged values.

# calculate the log-likelihood when lambda = 5
sum(dpois(x = x_data, lambda = 5, log = TRUE)) 
##  -68.11844