[SciPy-User] specifying range in scipy.stats.truncnorm

Joon Ro joonpyro@gmail....
Fri Jul 27 19:49:28 CDT 2012

On Fri 27 Jul 2012 01:39:50 PM CDT, josef.pktd@gmail.com wrote:
> On Fri, Jul 27, 2012 at 1:58 PM, Joon Ro <joonpyro@gmail.com> wrote:
>> On Fri 27 Jul 2012 12:07:15 PM CDT, josef.pktd@gmail.com wrote:
>>> On Fri, Jul 27, 2012 at 12:30 PM, Joon Ro <joonpyro@gmail.com> wrote:
>>>> Hi,
>>>> I tried to use scipy.stats.truncnorm and found the way to specifying the
>>>> parameters of truncated normal very confusing.
>>>> I expected a, b parameter to be the specification of the interval where I
>>>> want to truncate the distribution at, but it is not the case when the normal
>>>> I want to use is not standard.
>>>> According to the documentation, I need to standardize my values - for
>>>> example, if I want to have a truncated normal with mean 0.5, variance 1, on
>>>> [0, 1] interval, I need to do:
>>>> myclip_a = 0
>>>> myclip_b = 1
>>>> my_mean=0.5
>>>> my_std =1
>>>> a, b = (myclip_a - my_mean) / my_std, (myclip_b - my_mean) / my_std
>>>> rv = truncnorm(a, b, loc=my_mean, scale=my_std)
>>>> Which is unnecessarily complicated in my opinion. Since we have to provide
>>>> location and scale parameter anyway, why not make truncnorm to accept the
>>>> actual interval values (in this case, a, b = 0, 1) instead and do the
>>>> standardization internally? I think it would be more intuitive that way.
>>> I agree there are several cases of distributions where the
>>> parameterization is not very intuitive or common. The problem is loc
>>> and scale and the corresponding transformation of the support is done
>>> generically.
>>> So, I don't think it's possible to change this without a change in the
>>> generic setup for the distributions or writing a specific dispatch
>>> function or class that does the conversion.
>>> I think, changing the generic setup would break the standard behavior
>>> of distributions that have a predefined finite support limit, like
>>> those that are defined for positive real numbers, a=0, or rdist with
>>> a=-1, b=1.
>>> Josef
>> I just took a look at the code, and I agree.
>> I wonder if it would be possible to add a couple of more parameters (in
>> this case, representing the not-standardized interval) with default
>> None to the generic rv_continuous class and when they are passed
>> instead of a and b, let a distribution specific function do the
>> standardization and calculate a and b.
> a,b are set and would have to be adjusted in _argcheck.
> _argcheck is currently called only with the shape parameters, but not
> with loc and scale as argument. It would be possible to adjust this.
> My guess is that having _argcheck compensate for loc and scale should
> work. Having a possible change in behavior and extra parameters might
> get confusing. (distributions are instances and not classes, so care
> needs to be taken that there are no unwanted spillovers from one use
> to the next.)
> If you use frozen distributions, as in your initial example, then
> doing the reparameterization in the frozen class might be easier, then
> in the original classes.

I also think changing what a and b represent is the best way but I 
wonder if it is okay (for compatibility reasons)


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