Hi Pamphile,
You can also have a look at the output of
DistributionFactory.GetDiscreteMultivariateFactories() and
DistributionFactory.GetDiscreteUnivariateFactories() to get the list of all the
discrete distributions for which a parametric estimation method has been
implemented. Not exactly what you asked for, but rather close.
You mentioned the Multinomial distribution. Be aware of the fact that we don't
implement the textbook multinomial distribution (for which X_1+...+X_d=n)
because:1) This distribution is not absolutely continuous wrt the discrete
Lebesgues measure on {0,...,n}^d2) As such, it does not correspond to the
binomial distribution for any choice of the parameters3) Our definition allows
to recover the classical one using an obvious trick (set the d+1 component of
the probability vector to 0)
You have noticed that the UserDefined class allows to define any (ie arbitrary
dimension, arbitrary finite support size) discrete distribution with finite
support. It is why the list of discrete distributions is much shorter than the
list of absolutely continuous distributions.
For a given distribution, you can check if it is continuous
(myDist.isContinuous()), discrete (myDist.isDiscrete(), which is NOT the
negation of isContinuous()) or integral (myDist.isIntegral()), this last
property allowing to check if the support is part of a lattice, very useful eg
to apply Poisson's summation formula.
Using the Mixture class, you can easily build a distribution which is neither
continuous nor discrete, think about the waiting time at a traffic light as an
application.
You can get the discrete distribution of a linear combination of independent
discrete distributions using the RandomMixture class. It is limited to
univariate distributions for now.
A++
Régis
Le mercredi 8 novembre 2017 à 14:27:36 UTC+1, Julien Schueller | Phimeca
<[email protected]> a écrit :
Hi Roy,
There are several discrete distributions:
- Poisson
- Binomial
- Multinomial
- Dirac
- Geometric
- Skellam
- Bernoulli
- NegativeBinomial
- UserDefined
- ZipfMandelbrot
Maybe they should be highlighted in the doc.
j
De : [email protected] <[email protected]> de la part de
roy <[email protected]>
Envoyé : mercredi 8 novembre 2017 14:14:04
À : users
Objet : [ot-users] Discrete distribution Hi everyone,
I was looking at a way to have discrete distribution.From the doc there is no
discrete distribution (or I missed it) so I wanted to use scipy’s
distributionsand wrap them with ot.SciPyDistribution. But with randint I got
this issue :
>>> ot.SciPyDistribution(randint)Traceback (most recent call last): File
>>> "<stdin>", line 1, in <module> File
>>> "/Users/roy/Applications/miniconda3/envs/batman3/lib/python3.6/site-packages/openturns/model_copula.py",
>>> line 3047, in __init__ raise TypeError('Argument is not a scipy
>>> distribution')TypeError: Argument is not a scipy distribution
I tried commenting the raise and it seems to work as I expected. But I suppose
the raise is here for a reason.
Until then I am using this:
from scipy.stats import randintimport openturns as ot
rv = randint(10, 20)points = ot.Sample(10000, 1)for i in range(10000):
points[i] = (rv.rvs(),)
disc_dist = ot.UserDefined(points)
Thanks.
Sincerely,
Pamphile ROY
Chercheur doctorant en Quantification d’Incertitudes
CERFACS - Toulouse (31) - France
+33 (0) 5 61 19 31 57
+33 (0) 7 86 43 24 22
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