[SciPy-User] [SciPy-user] support for truncated normal distribution
josef.pktd@gmai...
josef.pktd@gmai...
Tue Mar 15 15:30:33 CDT 2011
On Tue, Mar 15, 2011 at 3:03 PM, Wes McKinney <wesmckinn@gmail.com> wrote:
> On Tue, Mar 15, 2011 at 2:58 PM, Robert Kern <robert.kern@gmail.com> wrote:
>> On Tue, Mar 15, 2011 at 13:45, Dr. Phillip M. Feldman
>> <pfeldman@verizon.net> wrote:
>>>
>>> I've noticed that there is no truncated normal distribution in NumPy, at
>>> least according to the following source:
>>>
>>> http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.mtrand.RandomState.html,
>>>
>>> I've written code to generate random deviates from a truncated normal
>>> distribution via acceptance-rejection, but this is inefficient when the
>>> acceptance probability is low. I assume that NumPy is generating standard
>>> normal deviates via the Ziggurat algorithm. That algorithm can be modified
>>> to produce random deviates from a truncated normal without the use of
>>> acceptance-rejection. I'd be very grateful if someone can implement this.
>>
>> No, we use the Box-Mueller transform, which is not easily truncated.
>>
>> --
>> Robert Kern
>>
>> "I have come to believe that the whole world is an enigma, a harmless
>> enigma that is made terrible by our own mad attempt to interpret it as
>> though it had an underlying truth."
>> -- Umberto Eco
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>
> I have an implementation here (using the inverse CDF method):
>
> https://github.com/wesm/statlib/blob/master/statlib/distributions.py#L12
>
> There is also scipy.stats.truncnorm (which I have not tested but assume works):
It`s using the generic rvs which is also inverse cdf method.
Josef
>
> Notes
> -----
> Truncated Normal distribution.
>
> The standard form of this distribution is a standard normal
> truncated to the
> range [a,b] --- notice that a and b are defined over the domain
> of the standard normal. To convert clip values for a specific mean and
> standard deviation use a,b = (myclip_a-my_mean)/my_std,
> (myclip_b-my_mean)/my_std
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