[SciPy-User] specifying range in scipy.stats.truncnorm
Fri Jul 27 13:39:50 CDT 2012
On Fri, Jul 27, 2012 at 1:58 PM, Joon Ro <email@example.com> wrote:
> On Fri 27 Jul 2012 12:07:15 PM CDT, firstname.lastname@example.org wrote:
>> On Fri, Jul 27, 2012 at 12:30 PM, Joon Ro <email@example.com> wrote:
>>> 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_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
>> 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.
> 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.
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