# What is the number of parameters needed for a joint probability distribution?

Let's suppose we have $4$ discrete random variables, say $X_1, X_2, X_3, X_4$, with $3,2,2$ and $3$ states, respectively.

Then the joint probability distribution would require $3 \cdot2 \cdot2 \cdot 3-1$ parameters (we don't know any independence relations). Considering the Chain Rule, and considering the fact that you need one parameter, $p$, for the marginal distribution of each node with two states, and $2$ for the ones with $3$ states, we have

$$P(X_4,X_3,X_2,X_1)=P(X_4 | X_3,X_2,X_1)P(X_3 | X_2,X_1)P(X_2 | X_1)P(X_1)$$

so we need $3 \cdot 2 \cdot 2 \cdot 2$ parameters for the first conditional probability distribution (as there are $2 \cdot 2 \cdot 3$ combinations of the first three variables and we need the $2$ parameters of $X_4$ for each one), $3 \cdot 2$ for the second one, $3$ for the third one and $2$ for the last one.

So... do we need $3 \cdot 2 \cdot 2 \cdot 2 +3 \cdot 2 + 3 +2$ parameters?

It is actually true that $3 \cdot 2 \cdot 2 \cdot 2 +3 \cdot 2 + 3 +2 =3 \cdot 2 \cdot 2 \cdot 3$ ?

• All your quantities are equal, except for the right-hand side of your last expression, from which you have to subtract 1 (as in your 2nd sentence). Jul 9, 2017 at 18:01

It takes $$3\times 2 \times 2 \times 3 = 36$$ numbers to write down a probability distribution on all possible values of these variables. They are redundant, because they must sum to $$1$$. Therefore the number of (functionally independent) parameters is $$35$$.

If you need more convincing (that was a rather hand-waving argument), read on.

By definition, a sequence of such random variables is a measurable function

$$\mathbf{X}=(X_1,X_2,X_3,X_4):\Omega\to\mathbb{R}^4$$

defined on a probability space $$(\Omega, \mathcal{F}, \mathbb{P})$$. By limiting the range of $$X_1$$ to a set of three elements ("states"), etc., you guarantee the range of $$\mathbf{X}$$ itself is limited to $$3\times 2\times 2 \times 3=36$$ possible values. Any probability distribution for $$\mathbf{X}$$ can be written as a set of $$36$$ probabilities, one for each one of those values. The axioms of probability impose $$36+1$$ constraints on those probabilities: they must be nonnegative ($$36$$ inequality constraints) and sum to unity (one equality constraint).

Conversely, any set of $$36$$ numbers satisfying all $$37$$ constraints gives a possible probability measure on $$\Omega$$. It should be obvious how this works, but to be explicit, let's introduce some notation:

• Let the possible values of $$X_i$$ be $$a_i^{(1)}, a_i^{(2)}, \ldots, a_i^{(k_i)}$$ where $$X_i$$ has $$k_i$$ possible values.

• Let the nonnegative numbers, summing to $$1$$, associated with $$\mathbf{a}=(a_1^{(i_1)}, a_2^{(i_2)}, a_3^{(i_3)}, a_4^{(i_4)})$$ be written $$p_{i_1i_2i_3i_4}$$.

• For any vector of possible values $$\mathbf{a}$$ for $$\mathbf{X}$$, we know (because random variables are measureable) that $$\mathbf{X}^{-1}(\mathbf{a}) = \{\omega\in\Omega\mid \mathbf{X}(\omega)=\mathbf{a}\}$$ is a measurable set (in $$\mathcal{F}$$). Define $$\mathbb{P}\left(\mathbf{X}^{-1}(\mathbf{a})\right) = p_{i_1i_2i_3i_4}.$$

It is trivial to check that $$\mathbb{P}$$ is an $$\mathcal{F}$$-measurable probability measure on $$\Omega$$.

The set of all such $$p_{i_1i_2i_3i_4}$$, with $$36$$ subscripts, nonnegative values, and summing to unity, form the unit simplex in $$\mathbb{R}^{36}$$.

We have thereby a established a natural one-to-one correspondence between the points of this simplex and the set of all possible probability distributions of all such $$\mathbf{X}$$ (regardless of what $$\Omega$$ or $$\mathcal{F}$$ might happen to be). The unit simplex in this case is a $$36-1=35$$-dimensional submanifold-with-corners: any continuous (or differentiable, or algebraic) coordinate system for this set requires $$35$$ numbers.

This construction is closely related to a basic tool used by Efron, Tibshirani, and others for studying the Bootstrap as well as to the influence function used to study M-estimators. It is called the "sampling representation."

To see the connection, suppose you have a batch of $$36$$ data points $$y_1, y_2, \ldots, y_{36}$$. A bootstrap sample consists of $$36$$ independent realizations from the random variable $$\mathbf X$$ that has a $$p_1=1/36$$ chance of equaling $$y_1$$, a $$p_2=1/36$$ chance of equaling $$y_2$$, and so on: it is the empirical distribution.

To understand the properties of the Bootstrap and other resampling statistics, Efron et al consider modifying this to some other distribution where the $$p_i$$ are no longer necessarily equal to one another. For instance, by changing $$p_k$$ to $$1/36 + \epsilon$$ and changing all the other $$p_j$$ ($$j\ne k$$) by $$-\epsilon/35$$ you obtain (for sufficiently small $$\epsilon$$) a distribution that represents overweighting the data value $$X_k$$ (when $$\epsilon$$ is positive) or underweighting it (when $$\epsilon$$ is negative) or even deleting it altogether (when $$\epsilon=-1/36$$), which leads to the "Jackknife".

As such, this representation of all the weighted resampling possibilities by means of a vector $$\mathbf{p} = (p_1,p_2, \ldots, p_{36})$$ allows us to visualize and reason about different resampling schemes as points on the unit simplex. The influence function of the value $$X_k$$ for any (differentiable) functional statistic $$t$$, for instance, is simply proportional to the partial derivative of $$t(X)$$ with respect to $$p_k$$.

### Reference

Efron and Tibshirani (1993), An Introduction to The Bootstrap (Chapters 20 and 21).

The number of parameters needed to represent a random variable is only defined with reference to a model, that is, a family of cumulative distribution functions equipped with a set of parameters that can be used to index them. For example, a normally distributed random variable with mean 3 and standard deviation 1 could be represented with a 0-parameter model (where the only legal distribution is $N(3, 1)$), a 2-parameter model (e.g., $N(μ, σ)$ where $μ$ and $σ$ are parameters), or a 4-parameter model (e.g., $N(μ_1, σ_1) + N(μ_2, σ_2)$).

• The question asks how many parameters are required by the family of all probability distributions for the random variable $(X_1,X_2,X_3,X_4)$. Because that family can be identified with the unit simplex in $\mathbb{R}^{3\times 2\times 2\times 3}$, there is a unique correct answer.
– whuber
Jul 10, 2017 at 13:43
• @whuber "The question asks how many parameters are required by the family of all probability distributions" — News to me. Jul 10, 2017 at 14:53
• @whuber But more to the point, any model with finitely many parameters can be expressed as a 1-parameter model, because you can encode a finite set of real numbers into a single real number by interleaving the decimal digits. Jul 10, 2017 at 16:10
• That's not right, because a parameter must establish more than a mere one-to-one relationship: it must be continuous. Note that I did not say all probability distributions: I said distributions for this random variable. The "joint probability distribution" referred to in the question must be one of those.
– whuber
Jul 10, 2017 at 17:35
• It is very hard to find formal definitions--almost all books gloss over the issues by assuming we know what kinds of parameters make sense. There are various ways to conceive of what I meant by "continuous." One was published by Peter McCullagh, What Is a Statistical Model? Annals of Statistics (2002) Vol. 30, No. 5, pp 1225-1310. He provides a mathematical formulation intended to supply definitions of the concepts of "a model 'making sense' and a parameter 'having a meaning'" (p. 1237). The parameterization you propose typically is not meaningful.
– whuber
Jul 10, 2017 at 20:21

I would get concrete here. Suppose one has this table, credit

| t    | w    | p(t,w) |
|------|------|--------|
| hot  | sun  | 0.4    |
| hot  | rain | 0.1    |
| cold | sun  | 0.2    |
| cold | rain | 0.3    |


To calculate all $$p(t,w)$$, do we need four params? Yes, or we can get away with three params and let the last param be 1 minus the sum of the other three params.

Now, what if $$t$$ and $$w$$ are independent? To generate the whole table, we only need $$2-1=1$$ param for $$t$$, let's say $$p(t=hot)$$ and $$2-1=1$$ param for $$w$$, say $$p(w=sun)$$.