Probability Distributions Flashcards

1
Q

When is a Bernoulli distribution used?

A

Whenever only two outcomes are

possible: one is identified with success and the other with failure.

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2
Q

What other distributions are formed using bernouill trials

A

Binomial
Geometric
Negative binomial

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3
Q

How is the sum of bernouilli iid RVs distributed

A

Binomial(n,p)

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4
Q

What is a poisson random variable usually used for

A

Time between events. Probability of success in poisson distribution is usually Quite small - used for rare events

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5
Q

What does the poisson limit theorem state

A

X is approximately distributed as poisson p for X~Bin(n,p/n) as n goes to infinity. So pmf of X tends to poisson pmf as n goes to infinity

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6
Q

What are two ways to show an RV is identically distributed

A

Shows cdfs are the same or mgfs are the same

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7
Q

What practical application can we take from the poisson limit theorem

A

For very big n instead of binomial we can use poisson distribution

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8
Q

Explain the continuous unfirom distribution

A

The continuous uniform distribution is defined by spreading mass
uniformly over an interval [a, b]

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9
Q

Explain the gamma family

A

Continuous distributions for outcomes in [0,infinity)

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10
Q

If n is an integer what si the value of gamma(n)

A

n-1 !

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11
Q

What is the relationship between gamma and exponential

A

Gamma(1, lamda) is an exponential(lamda) RV

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12
Q

What is the relationship between gamma and chi squared

A

Gamma (v/2,1/2) is a chi squared X^2(v) distribution

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13
Q

What is the relationship between weibull and exponential

A

If X ~ Exp(1)

W is weibull (theta, lamda) where W=(X^(1/theta))/lamda

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14
Q

Why is a normal distribution one of the most used distributions

A

It occurs naturally in a variety of contexts.

  • It is analytically tractable.
  • It has the familiar bell shape with symmetry property
  • Its parameters
  • CLT
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15
Q

What distributions can use Chebyshev and markov inequality

A

They make no assumptions about distribution. Used on any distribution

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