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The exponential distribution is a continuous probability distribution with probability density definded by:

f(x) = ke-kx for x â‰¥ 0

and

f(x) = 0 otherwise.

Q: What is exponential distribution?

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Why belong exponential family for poisson distribution

Exponential DistributionThe exponential distribution is a very commonly used distribution in reliability engineering. Due to its simplicity, it has been widely employed even in cases to which it does not apply. The exponential distribution is used to describe units that have a constant failure rate.

Exponential distribution is a function of probability theory and statistics. This kind of distribution deals with continuous probability distributions and is part of the continuous analogue of the geometric distribution in math.

The exponential distribution and the Poisson distribution.

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Related questions

Why belong exponential family for poisson distribution

Exponential DistributionThe exponential distribution is a very commonly used distribution in reliability engineering. Due to its simplicity, it has been widely employed even in cases to which it does not apply. The exponential distribution is used to describe units that have a constant failure rate.

Exponential distribution is a function of probability theory and statistics. This kind of distribution deals with continuous probability distributions and is part of the continuous analogue of the geometric distribution in math.

Poisson distribution or geometric distribution

The exponential distribution and the Poisson distribution.

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Continuous

It is a continuous parametric distribution belonging to the family of exponential distributions. It is also symmetric.

The hypoexponential distribution is two or more exponential distribution convolved together, so:hypo[x] = integral of A*exp(-A*x)*B*exp(-B*(t-x)) from 0 to t.If you have more stages you do more convolutions.

To derive the moment generating function of an exponential distribution, you can use the definition of the moment generating function E(e^(tX)) where X is an exponential random variable with parameter λ. Substitute the probability density function of the exponential distribution into the moment generating function formula and simplify the expression to obtain the final moment generating function for the exponential distribution, which is M(t) = λ / (λ - t) for t < λ.

the normal distribution is a bell shape and expeonential is rectangular

he uese it when he is lost like a hungry man