Marginal probabilities and densities
This is to help everybody, from those who study Basic Statistics up to Advanced Statistics ST2133.
Suppose in a box we have coins and banknotes of only two denominations: $1 and $5 (see Figure 1).
We pull one out randomly. The division of cash by type (coin or banknote) divides the sample space (shown as a square, lower left picture) with probabilities and (they sum to one). The division by denomination ($1 or $5) divides the same sample space differently, see the lower right picture, with the probabilities to pull out $1 and $5 equal to and , resp. (they also sum to one). This is summarized in the tables
|Variable 1: Cash type||Prob|
|Variable 2: Denomination||Prob|
Now we can consider joint events and probabilities (see Figure 2, where the two divisions are combined).
For example, if we pull out a random it can be a and $1 and the corresponding probability is The two divisions of the sample space generate a new division into four parts. Then geometrically it is obvious that we have four identities:
Adding over denominations:
Adding over cash types:
Formally, here we use additivity of probability for disjoint events
In words: we can recover own probabilities of variables 1,2 from joint probabilities.
Suppose we have two discrete random variables taking values and resp., and their own probabilities are Denote the joint probabilities Then we have the identities
(1) ( equations).
In words: to obtain the marginal probability of one variable (say, ) sum over the values of the other variable (in this case, ).
The name marginal probabilities is used for because in the two-dimensional table they arise as a result of summing table entries along columns or rows and are displayed in the margins.
Analogs for continuous variables with densities
Suppose we have two continuous random variables and their own densities are and Denote the joint density . Then replacing in (1) sums by integrals and probabilities by densities we get
In words: to obtain one marginal density (say, ) integrate out the other variable (in this case, ).