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

What is meant by a conditional distribution? A conditional distribution lists the relative frequency of each category of the response variable, given a specific value of the explanatory variable in a contingency table.

A conditional distribution is a distribution of values for one variable that exists when you specify the values of other variables. This type of distribution allows you to assess the dispersal of your variable of interest under specific conditions, hence the name.

First, to find the conditional distribution of X given a value of Y, we can think of fixing a row in Table 1 and dividing the values of the joint pmf in that row by the marginal pmf of Y for the corresponding value. For example, to find pX|Y(x|1), we divide each entry in the Y=1 row by pY(1)=1/2.

If we want to know the probability that a person prefers a certain sport given that they are male, then this is an example of a conditional distribution. The value of one random variable is known (the person is male), but the value of the other random variable is unknown (we don't know their favorite sport).

Marginal distributions are totals for each row or column in a two-way table (or joint distribution table), showing the distribution of one variable. Conditional distributions show the distribution of one variable given a condition on the other. They're usually in percentages.

Conditional distribution, on the other hand, is the probability distribution of certain values in the table expressed as percentages out of sums (or local totals) of certain rows or columns. So you're basically going one level down here. These row and column totals is what's given in the conditional distribution.

Joint probability is the probability of two events occurring simultaneously. Marginal probability is the probability of an event irrespective of the outcome of another variable. Conditional probability is the probability of one event occurring in the presence of a second event.

A conditional distribution is a distribution of values for one variable that exists when you specify the values of other variables. This type of distribution allows you to assess the dispersal of your variable of interest under specific conditions, hence the name.

The marginal distribution describes the likelihood of an event to occur, independent of others, while the conditional distribution describes the likelihood of an event to occur given the outcome of another.