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What else can I help you with?
How do you compute the probability distribution of a function of two Poisson random variables?
we compute it by using their differences
What is the difference between a probability mass function and a probability density function?
The probability mass function is used to characterize the distribution of discrete random variables, while the probability density function is used to characterize the distribution of absolutely continuous random variables. You might want to read more about this at www.statlect.com/prbdst1.htm (see the link below or on the right)
Which of the following is incorrect Select one a. Probability distribution is used to compute continuous random variables b. Probability distribution equals to one. c. Probability distribution is used?
b is incorrect while c is virtually meaningless.
What has the author T V Arak written?
T. V. Arak has written: 'Uniform limit theorems for sums of independent random variables' -- subject(s): Distribution (Probability theory), Limit theorems (Probability theory), Random variables, Sequences (Mathematics)
How do you determine marginal distribution?
Marginal distribution is determined by summing or integrating the joint distribution over the other variable(s). For a discrete random variable, this involves adding the probabilities of all outcomes for one variable while ignoring the others. For continuous random variables, it requires integrating the joint probability density function over the range of the other variables. This process provides the probability distribution of a single variable, reflecting its behavior independently of other variables.
Difference between variables and probability distribution?
Assuming you mean random variable here. A random variable is term that can take have different values. for example a random variable x that represent the out come of rolling a dice, that is x can equal 1,2,3,4,5,or 6. Think of probability distribution as the mapping of likelihood of the out comes from an experiment. In the dice case, the probability distribution will tell you that there 1/6 the time you will get 1, 2,3....,or 6. this is called uniform distribution since all the out comes have that same probability of occurring.
How many types of randam variables in Statistics?
In statistics, there are two main types of random variables: discrete random variables and continuous random variables. Discrete random variables take on a countable number of distinct values, such as the outcome of rolling a die. In contrast, continuous random variables can take on an infinite number of values within a given range, such as the height of individuals. Each type has its own probability distribution and methods of analysis.
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 gives the probability of each random variable?
The marginal probability distribution function.
Will the sum of two normally distributed random variables be normally distributed if the random variables are independent?
Yes, and the new distribution has a mean equal to the sum of the means of the two distribution and a variance equal to the sum of the variances of the two distributions. The proof of this is found in Probability and Statistics by DeGroot, Third Edition, page 275.
When does normal distribution occur?
The normal distribution occurs when a number of random variables, with independent distributions, are added together. No matter what the underlying probability distribution of the individual variables, their sum tends to the normal as their number increases. Many everyday measures are composed of the sums of small components and so they follow the normal distribution.
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.
