Hi Phil,

2012/10/19 Phil Steitz <phil.ste...@gmail.com>:
> On 10/19/12 12:20 AM, Sébastien Brisard wrote:
>> Hi Phil,
>>
>>> that might also force us to implement some useful multivariate
>>> integration algorithms :)
>>>
>> Is there any, general purpose multidimensional integration algorithm?
>> MC would be flexible enough, but I doubt this would be efficient enough...
>> How would you define the domain? Would you restrict it to products of
>> intervals, or more general domains?
>
> I have not really looked into multivariate integration algorithms.
> Note that we do not provide this convenience now for univariate
> distributions (though I did recently add some integration tests that
> verify consistency of the distribution and density impls).  I would
> define the cdf to take dimension vectors are arguments, so F(x_1,
> ..., x_n) = P(X_1 <=x_1, ..., X_n <= x_n).   Other things to
> consider are marginal and conditional densities and distributions.
> Here again, defaults could in theory be provided; but without
> actually hacking some examples, I am not sure it is practical.
>
Even in one-d, I would not recommend that we implement a default
integration scheme to be used in *all situations* on an unbounded
integration domain (-infty, x]. Indeed, I think it will always be
possible to find a distribution with a pdf which goes to zero so
slowly as x tends to -\infty that our integration scheme fails (or is
grossly in error). Or am I missing something?

Sébastien
>
> Phil
>>
>> Sébastien
>>
>>
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>
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