What is a probability mass function (PMF)?

A probability mass function p_X(x) gives the probability that a discrete random variable X takes exactly the value x. It is defined as p_X(x) = P(X = x). The PMF assigns probability directly to each value in the support of X, and the total of all these probabilities equals 1.

What two axioms must every valid PMF satisfy?

First, non-negativity: p_X(x) must be greater than or equal to zero for all x. Second, normalization: the sum of p_X(x) over all values in the support must equal exactly 1. Any function that fails either condition is not a valid probability mass function.

What is the difference between a PMF and a PDF?

A PMF describes discrete random variables, where probability concentrates at specific points and P(X = x) can be a nonzero value. A PDF describes continuous random variables, where probability spreads across intervals and P(X = x) equals zero at every individual point. PMF values are probabilities; PDF values are densities.

How are the PMF and CDF related?

The CDF F_X(x) is obtained by summing the PMF over all values less than or equal to x: F_X(x) equals the sum of p_X(k) for k less than or equal to x. The PMF is recovered from the CDF by taking the size of each jump: p_X(k) equals F_X(k) minus F_X(k-1). For discrete variables, the CDF is a step function.

How do you compute the expected value and variance from a PMF?

The expected value is E[X] = sum of k times p_X(k) over all values k in the support. The variance is Var(X) = sum of (k minus E[X]) squared times p_X(k), or equivalently E[X squared] minus (E[X]) squared. Both summaries are computed directly from the PMF by weighting outcomes by their probabilities.

Probability Mass Function (PMF)

Definition & Physical Intuition

What is a PMF?

A PMF gives the probability that a discrete random variable lands on each specific value.

Think of it like placing weights on a number line. Each possible outcome gets a weight equal to its probability. Where you put more weight, that outcome is more likely.

Example: Roll a fair die. Put weight

\frac{1}{6}

at positions 1, 2, 3, 4, 5, and 6. Each spot has equal probability.

Example: Roll a loaded die favoring 6. Put weight

\frac{1}{2}

at position 6, and split the remaining

\frac{1}{2}

among the other five faces.

The weights must sum to 1—that's your total probability budget.

PMF Weights Illustration

Mass vs. Density

The PMF is one of two ways to describe probability distributions. The key distinction is whether your variable takes discrete values or continuous values.

PMF (Mass): For discrete random variables. Probability concentrates at specific points. You can ask "What's

P(X = 5)

?" and get a number.

Example: Dice rolls, coin flips, number of customers.

PDF (Density): For continuous random variables. Probability spreads across intervals.

P(X = 5) = 0

for any exact point—you need ranges like

P(4.9 < X < 5.1)

.

Example: Height, weight, time measurements.

The PMF gives exact point probabilities. The PDF doesn't—it only gives probabilities over intervals.

Notation

The PMF uses specific notation to map outcomes to probabilities.

For random variable

X

:

p_X(x) = P(X = x)

p_X(x)

: The PMF of random variable

X

evaluated at value

x

.

P(X = x)

: The probability that

X

equals exactly

x

.

Why the Subscript?

When dealing with multiple random variables, the subscript keeps them distinct:

•

p_X(x)

: PMF of variable

X

•

p_Y(y)

: PMF of variable

Y

•

p_{N}(n)

: PMF of variable

N

With only one variable in context, you can drop the subscript and write

p(x)

.

See All Probability Symbols and Notations →

The Domain (Support)

The support of a PMF is the set of values where the probability is nonzero—the places where probability mass actually sits.

Formally: The support of

X

is

\{x : p_X(x) > 0\}

.

Think of it as the active zone. Outside the support,

p_X(x) = 0

. Nothing happens there.

Types of Support

Finite Support: The random variable can take only a limited number of values.

Example: A fair die. Support is

\{1, 2, 3, 4, 5, 6\}

. Six values, finite.

Example: Flip a coin 10 times, count heads. Support is

\{0, 1, 2, \ldots, 10\}

. Eleven values, finite.

Infinite Support: The random variable can take infinitely many values, but still countable.

Example: Flip a coin until you get heads. Count the number of flips. Support is

\{1, 2, 3, 4, \ldots\}

. Could go on forever.

Example: Number of emails you receive in a day (Poisson model). Support is

\{0, 1, 2, 3, \ldots\}

. No upper bound.

Why Support Matters

The support tells you:
• Where to look for probability
• What values are possible vs. impossible
• How to set up calculations (sums over the support)

If you're computing

P(X \in A)

for some set

A

, you only sum over values in

A

that are also in the support. Outside the support contributes nothing.

The Two Fundamental Axioms

Every valid PMF must satisfy two non-negotiable rules. Break either one, and you don't have a PMF.

Axiom 1: Non-Negativity

p_X(x) \geq 0 \text{ for all } x

Probabilities cannot be negative. Ever.

Each point gets zero or positive mass. If

p_X(5) = -0.2

, you've made an error. Fix it.

Axiom 2: Normalization

\sum_{x \in \text{Support}} p_X(x) = 1

All probabilities across the entire support must sum to exactly 1.

This is your total probability budget. You must allocate all of it, and you can't overspend.

Example: Fair die.

p_X(k) = \frac{1}{6}

for

k \in \{1,2,3,4,5,6\}

.

Check:

6 \times \frac{1}{6} = 1

. ✓

Example: Loaded die with

p_X(6) = \frac{1}{2}

and

p_X(k) = \frac{1}{10}

for

k \in \{1,2,3,4,5\}

.

Check:

\frac{1}{2} + 5 \times \frac{1}{10} = \frac{1}{2} + \frac{1}{2} = 1

. ✓

What These Axioms Guarantee

Together, these axioms ensure:
• No impossible probabilities (negative or greater than 1)
• Complete accounting (all outcomes covered)
• Consistency with basic probability theory

Violate either axiom, and your probability model breaks. These are the foundation everything else builds on.

Representations: The Three Views

A PMF can be represented in three equivalent ways. Each offers different insights into how probability mass distributes.

Graphical Representation

The PMF appears as a series of vertical bars (lollipop diagram) or dots on the x-y plane. The x-axis shows values of the random variable, the y-axis shows probability.

Key features:

Height of each bar represents probability at that point
Bars are discrete — isolated points, not continuous curves
All heights must be non-negative
Sum of all heights equals 1

Example: Fair die PMF shows six bars of equal height

\frac{1}{6}

at positions 1, 2, 3, 4, 5, 6.

Example: Binomial distribution with

n=10, p=0.5

shows bars concentrated near the center (around 5) with symmetric decay on both sides.

Functional Representation

The PMF is defined by an explicit mathematical formula

p_X(k)

.

Example: Fair die:

$$p_X(k) = \begin{cases}

\frac{1}{6} & \text{if } k \in \{1, 2, 3, 4, 5, 6\} \\

0 & \text{otherwise}

\end{cases}$$

Example: Geometric distribution:

p_X(k) = (1-p)^{k-1}p \text{ for } k \in \{1, 2, 3, \ldots\}

The functional form allows direct computation of probabilities and derivation of properties like expected value and variance.

Tabular Representation

The PMF can be displayed as a table listing each value and its probability.

Example: Roll two dice, sum the results:

Sum (k)	2	3	4	5	6	7	8	9	10	11	12
p_X(k)	1/36	2/36	3/36	4/36	5/36	6/36	5/36	4/36	3/36	2/36	1/36

Tables are especially useful when the PMF doesn't follow a simple formula or when presenting empirical data.

Why Three Views?

Graphical: Best for intuition and pattern recognition
Functional: Best for calculation and theoretical analysis
Tabular: Best for lookup and concrete examples

All three describe the same probability distribution, just from different perspectives. Choose the representation that best serves your immediate purpose.

Representation	Best for	Form	Example
Graphical	intuition; seeing shape, peaks, and tails	vertical bars (lollipops) or impulses at each value	six bars of height 1/6 for a fair die
Functional	calculation; deriving properties analytically	explicit mathematical formula p_X(k)	p_X(k) = (1−p)^k−1·p (geometric)
Tabular	lookup; concrete cases and empirical data	value-probability listing	the dice-sum table shown above

Building a PMF (From Story to Math)

Every PMF starts with a story—the situation, the process, the experiment. Your job is to translate that story into mathematical form.

The Construction Process

Step 1: Identify the Random Variable

What are you counting or measuring? Define it clearly.

Example: "Flip a coin 3 times, count heads" → Let

X

= number of heads.

Step 2: Determine the Support

What values can the random variable take?

Example:

X

can be 0, 1, 2, or 3. Support is

\{0, 1, 2, 3\}

.

Step 3: Assign Probabilities

For each value in the support, determine its probability. Use counting, combinatorics, symmetry, or physical reasoning.

Example: Fair coin, 3 flips.
•

P(X = 0) = \frac{1}{8}

(only TTT)
•

P(X = 1) = \frac{3}{8}

(HTT, THT, TTH)
•

P(X = 2) = \frac{3}{8}

(HHT, HTH, THH)
•

P(X = 3) = \frac{1}{8}

(only HHH)

Step 4: Verify the Axioms

Check non-negativity: All probabilities ≥ 0? Yes.

Check normalization: Do they sum to 1?

\frac{1}{8} + \frac{3}{8} + \frac{3}{8} + \frac{1}{8} = 1

✓

These are the two fundamental axioms every PMF must satisfy.

Step 5: Write the PMF

Express the pattern as a formula if possible, or as a table.

Formula:

p_X(k) = \binom{3}{k} \left(\frac{1}{2}\right)^3

for

k \in \{0,1,2,3\}

From Physical Setup to Mathematical Object

The key skill is moving from a concrete scenario to a precise probability function. Ask:
• What outcomes are possible?
• How likely is each one?
• Does the assignment satisfy the axioms?

Once built, the PMF becomes a complete mathematical description of the random variable's behavior.

Step	Action	Example: 3 coin flips, X = number of heads
1	Identify the random variable	let X = number of heads in 3 fair flips
2	Determine the support	support = {0, 1, 2, 3}
3	Assign a probability to each value	P(X=0) = 1/8; P(X=1) = 3/8; P(X=2) = 3/8; P(X=3) = 1/8
4	Verify the axioms	all probabilities ≥ 0 ✓; sum = 1/8 + 3/8 + 3/8 + 1/8 = 1 ✓
5	Write the PMF (formula or table)	p_X(k) = C(3, k) · (1/2)³ for k ∈ {0, 1, 2, 3}

Calculations Using the PMF

Once you have a PMF, you can compute probabilities for any event involving the random variable.

Single Point Probabilities

The PMF directly gives the probability of any specific value.

P(X = k) = p_X(k)

Example: Roll a fair die.

P(X = 4) = \frac{1}{6}

Range Probabilities

To find

P(X \in A)

for some set

A

, sum the PMF over all values in

A

.

P(X \in A) = \sum_{k \in A} p_X(k)

Example:

P(X \leq 3) = p_X(1) + p_X(2) + p_X(3) = \frac{1}{6} + \frac{1}{6} + \frac{1}{6} = \frac{1}{2}

Example:

P(2 \leq X \leq 5) = p_X(2) + p_X(3) + p_X(4) + p_X(5) = \frac{4}{6} = \frac{2}{3}

Complement Probabilities

To find

P(X \notin A)

, use the complement rule.

P(X \notin A) = 1 - P(X \in A)

Example:

P(X > 3) = 1 - P(X \leq 3) = 1 - \frac{1}{2} = \frac{1}{2}

The Core Pattern

Every probability question reduces to:
1. Identify which values satisfy the condition
2. Sum the PMF over those values
3. Simplify

The PMF is your complete toolkit. If you know

p_X(k)

for all

k

, you can answer any probability question about

X

.

Probability type	Formula	Fair-die example
Single point	P(X = k) = p_X(k)	P(X = 4) = 1/6
Range / event	P(X ∈ A) = Σ_{k ∈ A} p_X(k)	P(X ≤ 3) = 1/6 + 1/6 + 1/6 = 1/2
Complement	P(X ∉ A) = 1 − P(X ∈ A)	P(X > 3) = 1 − 1/2 = 1/2

Derived Properties: Location Measures

The PMF contains all the information about a random variable. From it, we can compute summary measures that describe where the distribution is centered.

Expected Value (Mean)

The expected value is the probability-weighted average of all possible values.

E[X] = \sum_{k \in \text{Support}} k \cdot p_X(k)

Think of it as the center of mass. Each value

k

contributes based on its probability weight.

Example: Fair die.

E[X] = 1 \cdot \frac{1}{6} + 2 \cdot \frac{1}{6} + 3 \cdot \frac{1}{6} + 4 \cdot \frac{1}{6} + 5 \cdot \frac{1}{6} + 6 \cdot \frac{1}{6} = \frac{21}{6} = 3.5

The average outcome over many rolls is 3.5.

Median

The median is the value that splits the probability in half. It's the point where

P(X \leq m) \geq 0.5

and

P(X \geq m) \geq 0.5

.

For discrete distributions, the median may not be unique if probability mass is concentrated at specific points.

Example: Fair die. The median is between 3 and 4, often reported as 3.5.

Mode

The mode is the value with the highest probability.

\text{mode} = \arg\max_k p_X(k)

Example: Loaded die with

p_X(6) = \frac{1}{2}

and

p_X(k) = \frac{1}{10}

for

k \in \{1,2,3,4,5\}

. Mode is 6.

A distribution can have multiple modes if several values share the maximum probability.

Why These Matter

Location measures summarize where the distribution sits on the number line. They answer: "What's a typical value?" Each measure captures a different notion of "typical."

Derived Properties: Spread Measures

While location tells you where the distribution is centered. Spread tells you how much the values vary around that center.

Variance

Variance quantifies the average squared deviation from the mean. It's calculated directly from the PMF:

\text{Var}(X) = \sum_{k \in \text{Support}} (k - E[X])^2 \cdot p_X(k)

Each value's squared distance from the mean is weighted by its probability. High-probability values far from the mean increase variance significantly.

Computational shortcut:

\text{Var}(X) = E[X^2] - (E[X])^2

Standard Deviation

Standard deviation is simply

\sqrt{\text{Var}(X)}

. It measures spread in the original units, making it more interpretable than variance.

A die with

\text{SD}(X) \approx 1.71

means outcomes typically deviate about 1.7 units from the mean.

Range

The range is the span of the support: maximum value minus minimum value.

For a fair die: Range =

6 - 1 = 5

Range ignores probability—it treats rare and common outcomes equally. A single extreme value in the support sets the range, regardless of how unlikely it is.

What Spread Reveals

Two distributions can share the same mean but behave completely differently. One might cluster tightly around the center; another might scatter values widely. Spread measures capture this distinction. They tell you how predictable individual outcomes are.

Measure	Category	What it captures	Formula from the PMF
Expected value (mean)	Location	center of mass — the probability-weighted average outcome	E[X] = Σ_k k · p_X(k)
Median	Location	value that splits the probability into halves	smallest m with P(X ≤ m) ≥ 0.5
Mode	Location	the most likely value (highest PMF)	argmax_k p_X(k)
Variance	Spread	average squared deviation from the mean	Var(X) = Σ_k (k − E[X])² · p_X(k)
Standard deviation	Spread	spread expressed in the same units as X	√Var(X)
Range	Spread	span of the support (ignores probability)	max(support) − min(support)

Transformations

If we take some random variable

X

and apply function

g

the result

Y=g(X)

will be a new random variable.

The Transformation Process

Start with random variable

X

with PMF

p_X(x)

.

Apply function

g

to get

Y = g(X)

.

The new PMF

p_Y(y)

is found by collecting all the probability mass from

X

values that map to each

y

value.

p_Y(y) = \sum_{x: g(x) = y} p_X(x)

Sum over all

x

values that produce the same

y

after transformation.

Linear Transformations

For

Y = aX + b

where

a \neq 0

:

p_Y(y) = p_X\left(\frac{y - b}{a}\right)

The support shifts and scales accordingly.

Example: If

X

is a fair die, then

Y = 2X

has support

\{2, 4, 6, 8, 10, 12\}

with

p_Y(k) = \frac{1}{6}

for each value.

Example:

Y = X + 3

shifts the die to support

\{4, 5, 6, 7, 8, 9\}

with probabilities unchanged.

Non-Injective Transformations

When multiple

x

values map to the same

y

, their probabilities combine.

Example:

X

is a fair die,

Y = (X - 3.5)^2

(squared distance from mean).

Multiple values map to the same squared distance:
•

p_Y(6.25) = p_X(1) + p_X(6) = \frac{1}{6} + \frac{1}{6} = \frac{1}{3}

•

p_Y(2.25) = p_X(2) + p_X(5) = \frac{1}{3}

•

p_Y(0.25) = p_X(3) + p_X(4) = \frac{1}{3}

Effect on Expected Value

For any transformation

g

:

E[g(X)] = \sum_{x} g(x) \cdot p_X(x)

You don't need to find

p_Y(y)

first. Work directly with the original PMF.

Linear transformations follow:

E[aX + b] = aE[X] + b

Key Insight

Transformations create new random variables, but the underlying probability structure stays connected to the original PMF. You're redistributing the same total probability mass, just arranging it differently.

Connection to CDF

The PMF and the CDF (Cumulative Distribution Function) are two ways to describe the same probability distribution. Each contains complete information about the random variable.

From PMF to CDF

The CDF accumulates probability from the PMF:

F_X(x) = P(X \leq x) = \sum_{k \leq x} p_X(k)

The CDF at any point equals the sum of all PMF values up to and including that point.

Example: Fair die.

$F_X(3) = p_X(1) + p_X(2) + p_X(3) = \frac{1}{6} + \frac{1}{6} + \frac{1}{6} = \frac{1}{2}$
$F_X(5.7) = p_X(1) + \cdots + p_X(5) = \frac{5}{6}$

The CDF is a step function for discrete variables—it jumps at each value in the support, with jump height equal to the PMF at that point.

From CDF to PMF

Recover the PMF by taking differences in the CDF:

p_X(k) = F_X(k) - F_X(k^-)

where

k^-

represents the value just before

k

in the support.

For the fair die:

p_X(3) = F_X(3) - F_X(2) = \frac{3}{6} - \frac{2}{6} = \frac{1}{6}

Why Both Exist

The PMF provides the probability of the random variable taking exactly a particular value and thus shows how probability distributes among all individual values of the domain.

The CDF answers the question: "What's the probability of at most this value?" and shows how probability accumulates across the domain.

Different questions favor different representations. The PMF is more direct for point probabilities. The CDF is better for cumulative and range probabilities. Both describe the same underlying distribution completely.

PMFs of Common Discrete Distributions

The standard discrete distributions each have distinct PMF shapes and behaviors.

Binomial Distribution

PMF:

p_X(k) = \binom{n}{k} p^k (1-p)^{n-k}

for

k \in \{0, 1, \ldots, n\}

The PMF is symmetric when

p = 0.5

, creating a bell-shaped pattern centered at

\frac{n}{2}

. For

p < 0.5

, mass concentrates toward 0, creating right skew. For

p > 0.5

, mass concentrates toward

n

, creating left skew. The peak occurs near

np

, which is also the expected value. As

n

increases, the PMF spreads wider but maintains its shape relative to

p

. The binomial coefficient

\binom{n}{k}

creates the characteristic rise and fall pattern, with probabilities increasing until the mode and then decreasing. For extreme values of

p

near 0 or 1, the distribution becomes highly skewed with most mass concentrated at one end.

Read More About Binomial Distribution

Geometric Distribution

PMF:

p_X(k) = (1-p)^{k-1} p

for

k \in \{1, 2, 3, \ldots\}

The PMF decreases monotonically—it never increases. Maximum mass always sits at

k=1

, the first possible value. Each subsequent value has probability

(1-p)

times the previous, creating exponential decay. For

p

close to 1, the PMF drops sharply, concentrating almost all mass at small values. For

p

close to 0, the PMF decays slowly, spreading mass across many values. The geometric PMF has the memoryless property: the shape of the tail remains proportional regardless of where you start. Unlike bounded distributions, the tail extends infinitely but thins out rapidly. The ratio between consecutive probabilities is constant:

\frac{p_X(k+1)}{p_X(k)} = (1-p)

Poisson Distribution

PMF:

p_X(k) = \frac{\lambda^k e^{-\lambda}}{k!}

for

k \in \{0, 1, 2, \ldots\}

The PMF starts at

p_X(0) = e^{-\lambda}

, rises to a peak near

\lambda

, then decays toward zero. For small

\lambda

(less than 1), the mode is at 0 and the PMF shows strong right skew. For moderate

\lambda

(around 5-10), the PMF becomes roughly symmetric and bell-shaped. For large

\lambda

(greater than 20), the PMF looks nearly normal. The rise-and-fall pattern is governed by the competition between

\lambda^k

(which grows) and

k!

(which grows faster). The peak shifts rightward as

\lambda

increases, and the spread increases proportionally—both mean and variance equal

\lambda

. The Poisson PMF never reaches zero for any finite

k

, though it becomes negligibly small far from

\lambda

.

Read More About Poisson Distribution

Negative Binomial Distribution

PMF:

p_X(k) = \binom{k-1}{r-1} p^r (1-p)^{k-r}

for

k \in \{r, r+1, r+2, \ldots\}

The PMF starts at

k=r

(the minimum possible value) and typically decreases from there, though for small

p

it may first increase slightly before declining. This is essentially a sum of

r

independent geometric random variables, so its shape resembles a widened, shifted geometric distribution. As

r

increases, the mode shifts rightward and the distribution spreads out. The parameter

p

controls the decay rate: high

p

produces fast decay and concentration near

r

, while low

p

produces slow decay and a long tail. When

r=1

, this reduces exactly to the geometric distribution. The PMF has heavier tails than the Poisson, making it useful for overdispersed count data. The ratio test shows probabilities eventually decrease monotonically, though the initial behavior depends on both parameters.

Read More About Negative Binomial Distribution

Hypergeometric Distribution

PMF:

p_X(k) = \frac{\binom{K}{k}\binom{N-K}{n-k}}{\binom{N}{n}}

The PMF is unimodal with a peak near

n \cdot \frac{K}{N}

, the expected proportion of successes. The shape closely resembles the binomial distribution but is slightly more concentrated—sampling without replacement reduces variance. Unlike binomial, the hypergeometric support is finite and bounded by the population constraints:

k

cannot exceed either

K

(total successes available) or

n

(sample size). As the population size

N

grows relative to sample size

n

, the hypergeometric PMF converges to binomial with

p = \frac{K}{N}

. For small populations or large samples, the distinction from binomial becomes significant. The PMF is symmetric when

K = \frac{N}{2}

, left-skewed when

K > \frac{N}{2}

, and right-skewed when

K < \frac{N}{2}

. The finite population creates a correction factor that shrinks variance compared to binomial sampling with replacement.

Read More About Hypergeometric Distribution

Discrete Uniform Distribution

PMF:

p_X(k) = \frac{1}{n}

for

k \in \{a, a+1, \ldots, b\}

where

n = b - a + 1

The PMF is completely flat—constant probability across the entire support. No value is more likely than any other, making this the only distribution with no peak or mode (every value is equally modal). This represents maximum uncertainty given only knowledge of the support boundaries. The PMF has zero skewness and is perfectly symmetric around the midpoint

\frac{a+b}{2}

. Adding or removing even one value from the support changes all probabilities—with

n

values, each gets exactly

\frac{1}{n}

of the total probability mass. The uniform PMF serves as a reference distribution: deviations from uniformity indicate structure or bias in the data. Despite its simplicity, the discrete uniform appears frequently in practice: fair dice, random selection, lottery draws. The PMF provides no information about which specific value will occur—all are equally plausible.

Read More About Discrete Uniform Distribution

PMF Patterns

Each PMF has characteristic behavior: where mass concentrates, how it spreads, whether it's symmetric or skewed, how parameters affect shape. These patterns reflect the underlying random process and determine the distribution's expected value and variance. Recognizing PMF shapes helps identify which distribution models your data.

Distribution	PMF	Support	Shape & key behavior
Binomial(n, p)	C(n, k) · p^k(1−p)^n−k	{0, 1, …, n}	bell-shaped at p = 0.5; peak near np; skewed when p ≠ 0.5
Geometric(p)	(1−p)^k−1 · p	{1, 2, 3, …}	monotonically decreasing; peak at k = 1; exponential decay
Poisson(λ)	λ^k · e^−λ / k!	{0, 1, 2, …}	rises to a peak near λ then decays; approximately normal for large λ
Negative Binomial(r, p)	C(k−1, r−1) · p^r(1−p)^k−r	{r, r+1, r+2, …}	sum of r geometrics; heavier tail than Poisson; reduces to geometric for r = 1
Hypergeometric(N, K, n)	C(K, k) · C(N−K, n−k) / C(N, n)	bounded by N, K, n (max(0, n−N+K) to min(n, K))	unimodal; peak near n · K/N; more concentrated than binomial (finite population)
Discrete Uniform(a, b)	1 / (b − a + 1)	{a, a+1, …, b}	flat — every value equally likely; no peak

Interactive visualization of six fundamental discrete distributions

Discrete Uniform

Equal probability for finite outcomes

Minimum Value (a): 1

Maximum Value (b): 6

Explanation

A discrete uniform distribution assigns equal probability to each value in a finite range. The probability mass function is $P(X = k) = \frac{1}{b - a + 1}$ for $a \leq k \leq b$ . The expected value is $E[X] = \frac{a + b}{2}$ , and the variance is $\text{Var}(X) = \frac{n^2 - 1}{12}$ , where $n = b - a + 1$ . Common examples include rolling a fair die, selecting a random card from a deck, or generating a random number from a finite range.

Common Mistakes

Confusing PMF with PDF

The PMF applies only to discrete random variables. For continuous variables, you need a PDF (Probability Density Function). The PMF gives exact point probabilities:

P(X = k)

. The PDF does not—for continuous variables,

P(X = x) = 0

for any specific point.

Don't apply PMF formulas to continuous data, and don't try to use PDF methods on discrete outcomes.

Forgetting to Check the Axioms

Not every function is a valid PMF. Before using any assignment as a PMF, verify:

$p_X(k) \geq 0$ for all $k$
$\sum_k p_X(k) = 1$

If either fails, you don't have a valid probability model. See axioms.

Summing Over the Wrong Set

When computing

P(X \in A)

, sum only over values in both

A

and the support. Don't include values outside the support—they contribute zero anyway. Don't skip values inside the support that belong to

A

.

Example: For a die,

P(X > 4) = p_X(5) + p_X(6)

, not

p_X(4) + p_X(5) + p_X(6)

.

Treating Probabilities as Fixed

The PMF depends on parameters. Change the parameters, and the entire PMF changes. A binomial PMF with

p = 0.3

looks completely different from one with

p = 0.7

, even with the same

n

.

Don't assume a distribution has one "standard" PMF—parameters matter.

Confusing Support with Domain

The support is where

p_X(k) > 0

. Outside the support,

p_X(k) = 0

. The PMF is technically defined everywhere, but only the support matters for calculations.

Don't waste time summing over values where the PMF is zero.

Ignoring Discrete Structure

Discrete random variables take isolated values, not continuous ranges.

P(2 < X < 3)

might be zero if nothing sits between 2 and 3. Don't treat discrete variables as if they vary smoothly.

Always check whether your variable is discrete or continuous before choosing methods.

Mistake	Wrong thinking	Correct view
Confusing PMF with PDF	applying PMF formulas to continuous data	the PMF is discrete-only; for continuous variables use a PDF, and P(X = x) = 0 for any single point
Skipping axiom verification	assuming any function is a valid PMF	check p_X(k) ≥ 0 everywhere and Σ p_X(k) = 1 before using
Summing over the wrong set	including non-support values, or skipping support values that belong to A	sum only over values in both A and the support — outside the support contributes zero
Treating probabilities as fixed	assuming a distribution has one “standard” PMF	parameters change the PMF entirely — binomial with p = 0.3 looks nothing like p = 0.7
Confusing support with domain	summing over the whole real line, or over values where p_X is zero	only the support matters; outside it the PMF is zero and contributes nothing
Ignoring discrete structure	treating discrete variables as smooth or continuous	discrete values are isolated; P(2 < X < 3) may be exactly 0 if no value sits between 2 and 3

PMF at a Glance

The page has unpacked the probability mass function from many angles — from its defining axioms and three modes of representation through summary measures, transformations, and the family of common discrete distributions. The table below collects the essentials into a single reference card, pairing each aspect of the PMF with its concise statement and a concrete formula or example.

Aspect	Statement	Example or formula
What a PMF is	a function p_X(x) = P(X = x) for a discrete random variable	fair die: p_X(k) = 1/6 for k ∈ {1, …, 6}
Support	the set of values where p_X(x) > 0	finite ({1, …, 6}) or infinite countable ({1, 2, 3, …})
Two axioms	non-negative everywhere; total over the support equals 1	p_X(k) ≥ 0 for all k; Σ p_X(k) = 1
Three representations	graphical, functional, tabular — the same PMF viewed three ways	bars at each value; a formula; a value-probability table
Computing event probabilities	sum the PMF over the values that make up the event	P(X ≤ 3) for a die = p_X(1) + p_X(2) + p_X(3) = 1/2
Summary measures	location (mean, median, mode) and spread (variance, SD, range) — all computed from the PMF	E[X] = Σ k · p_X(k); Var(X) = Σ (k − E[X])² · p_X(k)
Connection to the CDF	accumulate the PMF to get the CDF; differ to recover the PMF	F_X(x) = Σ_{k ≤ x} p_X(k); p_X(k) = F_X(k) − F_X(k⁻)
Common forms	named discrete distributions, each with a characteristic PMF shape	binomial, geometric, Poisson, negative binomial, hypergeometric, discrete uniform

Probability Mass Functions (PMF)

Probability in the Discrete Case

Key Terms

Definition & Physical Intuition

What is a PMF?

Mass vs. Density

Notation

Why the Subscript?

The Domain (Support)

Types of Support

Why Support Matters

The Two Fundamental Axioms

Axiom 1: Non-Negativity

Axiom 2: Normalization

What These Axioms Guarantee

Representations: The Three Views

Graphical Representation

Functional Representation

Tabular Representation

Why Three Views?

Building a PMF (From Story to Math)

The Construction Process

From Physical Setup to Mathematical Object

Calculations Using the PMF

Single Point Probabilities

Range Probabilities

Complement Probabilities

The Core Pattern

Derived Properties: Location Measures

Expected Value (Mean)

Median

Mode

Why These Matter

Derived Properties: Spread Measures

Variance

Standard Deviation

Range

What Spread Reveals

Transformations

The Transformation Process

Linear Transformations

Non-Injective Transformations

Effect on Expected Value

Key Insight

Connection to CDF

From PMF to CDF

From CDF to PMF

Why Both Exist

PMFs of Common Discrete Distributions

Binomial Distribution

Geometric Distribution

Poisson Distribution

Negative Binomial Distribution

Hypergeometric Distribution

Discrete Uniform Distribution

PMF Patterns

Discrete Uniform

Explanation

Common Mistakes

Confusing PMF with PDF

Forgetting to Check the Axioms

Summing Over the Wrong Set

Treating Probabilities as Fixed

Confusing Support with Domain

Ignoring Discrete Structure

PMF at a Glance