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distfun >> distfun > Normal > distfun_norminv

distfun_norminv

Normal Inverse CDF

Calling Sequence

x = distfun_norminv ( p , mu , sigma )
x = distfun_norminv ( p , mu , sigma , lowertail )

Parameters

p :

a matrix of doubles, the probability. Must be in the range [0,1].

mu :

a matrix of doubles, the mean.

sigma :

a matrix of doubles, the standard deviation. sigma>0.

lowertail :

a 1-by-1 matrix of booleans, the tail (default lowertail=%t). If lowertail is true (the default), then considers P(X<=x) otherwise P(X>x).

x :

a matrix of doubles, the outcome

Description

Computes the inverse Normal cumulated probability distribution function of the Normal (Laplace-Gauss) function.

Any scalar input argument is expanded to a matrix of doubles of the same size as the other input arguments.

Examples

// Test with default mu, sigma
x = distfun_norminv ( [0.5 0.9 0.7] , 0 , 1 )
expected = [ ..
0.000000000000000 , ..
1.281551565544601 , ..
0.524400512708041 ..
]

// Test argument expansion
x=distfun_norminv([0.5 0.9 0.7],1.0,2.0)
expected = [ ..
1.00000000000000 , ..
3.56310313108920 , ..
2.04880102541608 ..
]

// Test with expanded arguments
x=distfun_norminv([0.5 0.9 0.7],[1 1 1],[2 2 2])
expected = [ ..
1.00000000000000 , ..
3.56310313108920 , ..
2.04880102541608 ..
]

// See upper tail
x=distfun_norminv(0.0001,1.,2.,%f)
// See an extreme case
x=distfun_norminv(1.e-20,1.,2.,%f)

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