You simply have a function with two (or more) arguments which are continuous.
For example, z = p(x,y) would be a surface in 3 dimensions where x and y are the values taken by the two variables, X and Y respectively, and z is the probability associated with them.
w = g(x,y,z) would be a hyper-surface in 4-dimensional space and so on.
You have a function with two arguments (inputs). After that, the calculations depend on whether or not the two random variables are independent. If they are then the joint distribution is simple the product of the individual distribution. But if not, you have some serious mathematics ahead of you!
In some situationsX is continuous but Y is discrete. For example, in a logistic regression, one may wish to predict the probability of a binary outcome Y conditional on the value of a continuously-distributed X. In this case, (X, Y) has neither a probability density function nor a probability mass function in the sense of the terms given above. On the other hand, a "mixed joint density" can be defined in either of two ways:Formally, fX,Y(x, y) is the probability density function of (X, Y) with respect to the product measure on the respective supports of X and Y. Either of these two decompositions can then be used to recover the joint cumulative distribution function:The definition generalizes to a mixture of arbitrary numbers of discrete and continuous random variables.
Suppose you have two random variables, X and Y and their joint probability distribution function is f(x, y) over some appropriate domain. Then the marginal probability distribution of X, is the integral or sum of f(x, y) calculated over all possible values of Y.
It is the integral (or sum) of the joint probability distribution function of the two events, integrated over the domain in which the condition is met.
Joint probability is the probability that two or more specific outcomes will occur in an event. An example of joint probability would be rolling a 2 and a 5 using two different dice.
You have a function with two arguments (inputs). After that, the calculations depend on whether or not the two random variables are independent. If they are then the joint distribution is simple the product of the individual distribution. But if not, you have some serious mathematics ahead of you!
If f(x, y) is the joint probability distribution function of two random variables, X and Y, then the sum (or integral) of f(x, y) over all possible values of y is the marginal probability function of x. The definition can be extended analogously to joint and marginal distribution functions of more than 2 variables.
Let X and Y be two random variables.Case (1) - Discrete CaseIf P(X = x) denotes the probability that the random variable X takes the value x, then the joint probability of X and Y is P(X = x and Y = y).Case (2) - Continuous CaseIf P(a < X < b) is the probability of the random variable X taking a value in the real interval (a, b), then the joint probability of X and Y is P(a < X< b and c < Y < d).Basically joint probability is the probability of two events happening (or not).
In some situationsX is continuous but Y is discrete. For example, in a logistic regression, one may wish to predict the probability of a binary outcome Y conditional on the value of a continuously-distributed X. In this case, (X, Y) has neither a probability density function nor a probability mass function in the sense of the terms given above. On the other hand, a "mixed joint density" can be defined in either of two ways:Formally, fX,Y(x, y) is the probability density function of (X, Y) with respect to the product measure on the respective supports of X and Y. Either of these two decompositions can then be used to recover the joint cumulative distribution function:The definition generalizes to a mixture of arbitrary numbers of discrete and continuous random variables.
Suppose you have two random variables, X and Y and their joint probability distribution function is f(x, y) over some appropriate domain. Then the marginal probability distribution of X, is the integral or sum of f(x, y) calculated over all possible values of Y.
It is the integral (or sum) of the joint probability distribution function of the two events, integrated over the domain in which the condition is met.
The joint probability function for two variables is a probability function whose domain is a subset of two dimensional space. The joint probability function for discrete random variables X and Y is given aspr(x, y) = pr(X = x and Y = y). If X and Y are independent random variables then this will equal pr(X =x)*pr(Y = y).For continuous variables, the joint funtion is defined analogously:f(x, y) = pr(X < x and Y < y).
Joint probability is the probability that two or more specific outcomes will occur in an event. An example of joint probability would be rolling a 2 and a 5 using two different dice.
The joint probability function for two variables is a probability function whose domain is a subset of two dimensional space. The joint probability function for discrete random variables X and Y is given aspr(x, y) = pr(X = x and Y = y). If X and Y are independent random variables then this will equal pr(X =x)*pr(Y = y).For continuous variables, the joint funtion is defined analogously:f(x, y) = pr(X < x and Y < y).
American Jewish Joint Distribution Committee was created in 1914.
The joint probability of two discrete variables, X and Y isP(x, y) = Prob(X = x and Y = y) and it is defined for each ordered pair (x,y) in the event space.The conditional probability of X, given that Y is y is Prob[(X, Y) = (x, y)]/Prob(Y = y) or equivalently,Prob(X = x and Y = y)/Prob(Y = y)The marginal probability of X is simply the probability of X. It can be derived from the joint distribution by summing over all possible values of Y.
A joint probability can have a value greater than one. It can only have a value larger than 1 over a region that measures less than 1.