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How do you compute the probability distribution of a function of two Poisson random variables?
we compute it by using their differences
Can the Poisson distribution be a continuous random variable or a discrete random variable?
True
What is the difference between the normal distribution and Poisson distribution?
The normal distribution is a continuous probability distribution that describes the distribution of real-valued random variables that are distributed around some mean value.The Poisson distribution is a discrete probability distribution that describes the distribution of the number of events that occur within repeated fixed time intervals, where the mean frequency is a known value, and each interval is independent of the prior interval(s)/event(s).
If the outcomes of a random variable follow a Poisson distribution then their?
means equal the standard deviation
What is the probability that a Poisson random variable x is equal to 5...?
It depends on the parameter - the mean of the distribution.
How chi square distribution is extension of normal distribution?
Given "n" random variables, normally distributed, and the squared values of these RV are summed, the resultant random variable is chi-squared distributed, with degrees of freedom, k = n-1. As k goes to infinity, the resulant RV becomes normally distributed. See link.
What is the importance of distribution functions to stochastic processes?
Stochastic processes are families of random variables. Real-valued (i.e., continuous) random variables are often defined by their (cumulative) distribution function.
How did the normal distribution get its name?
According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.
Which distribution do not have mean?
The Cauchy or Cauchy-Lorentz distribution. The ratio of two Normal random variables has a C-L distribution.
What is the Probability density function of Poisson distribution?
If a random variable X has a Poisson distribution with parameter l, then the probability that X takes the value x isPr(X = x) = lx*e-l/x! for x = 0, 1, 2, 3, ...
How many random variables are needed to plot a probability distribution?
A probability density function can be plotted for a single random variable.
The usual sampling distributionof the difference between means is a what?
There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.
