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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 as
pr(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).
What else can I help you with?
What is marginal probability function?
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.
What is joint probability?
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).
What gives the probability of each random variable?
The marginal probability distribution function.
Can a joint probability have a value greater than 1?
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.
How do you find probability from probability generating function?
you can just ask the question on ask .com
What is function joint?
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).
What is marginal probability function?
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.
What is the conditional expectation function?
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.
Example of joint probability?
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.
What the difference between a probability distribution and a probability function?
None. The full name is the Probability Distribution Function (pdf).
What the difference and relationship between a probability distribution and a probability function?
They are the same. The full name is the Probability Distribution Function (pdf).
How do you calculation joint probability?
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!
What is the difference between probability distribution and probability density function?
A probability density function assigns a probability value for each point in the domain of the random variable. The probability distribution assigns the same probability to subsets of that domain.
What can be determined using theoretical probability?
The probability distribution function.
How do you calculate likelihood?
Likelihood is calculated by assessing the probability of observing the given data under a specific statistical model. Mathematically, it is expressed as the likelihood function, which is the joint probability of the observed data as a function of the model parameters. For independent observations, the likelihood is the product of the probabilities for each observation. Maximizing the likelihood function helps in estimating the parameters that best fit the data.
What is marginal probability?
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.
Is Probability a function?
Yes.
