James Andrew Cook wrote:
In particular, functions such as 'uniform' and 'normal' which directly construct RVars are very useful in
defining the rvar implementation of other types. I have been reluctant to drop the rvar function from the
Distribution class because it is very useful to be able
On Jun 4, 2010, at 9:42 PM, wren ng thornton wrote:
---unless, perhaps, you have a way of deriving a definition of rvarT from
rvar. If so, then there could be efficiency issues in the other direction. I
could see some people just giving a pretty implementation of rvar and using
the
On Jun 3, 2010, at 10:03 PM, wren ng thornton wrote:
Though, since RVar is a synonym for RVarT, I can't imagine why rvar is a
method instead of a shorthand defined outside of the class. (If RVar were
primitive then I could imagine performance reasons, but since it isn't...)
The reason for
On Jun 4, 2010, at 1:19 AM, Alexander Solla wrote:
We don't necessarily have to compute the inverse of the distribution via
sampling to do it. It can be done algebraically, in terms of the convolution
operator. Since the types are enumerated, wouldn't something like... work?
-- A set
Announcing the 0.1.0.0 release of the random-fu library for random
number generation[1]. This release hopefully stabilizes the core
interfaces (those exported from the base module Data.Random).
Warning to anyone upgrading from earlier releases: 'Discrete' has been
renamed 'Categorical', the
On Jun 3, 2010, at 6:34 AM, mo...@deepbondi.net wrote:
Announcing the 0.1.0.0 release of the random-fu library for random
number generation[1]. This release hopefully stabilizes the core
interfaces (those exported from the base module Data.Random).
Great work, I'm upgrading now.
The only
The only feature suggestion I can suggest is the addition of a
convolution operator to combine distributions (reified as RVar's in
this implementation, though of course the difference between a random
variable over a distribution and the distribution is rather thin)
I don't think I
On Jun 3, 2010, at 4:19 PM, mo...@deepbondi.net wrote:
I don't think I understand. My familiarity with probability theory is
fairly light. Are you referring to the fact that the PDF of the sum
of
random variables is the convolution of their PDFs? If so, the sum of
random variables can
On the other hand, it might be kind of nice if RVar's knew which PDF
they are over. It's hard for me to see how that would be done with
Haskell.
If anyone knows a way this could be done while still allowing general
functions to be mapped over RVars, I'd love to hear about it. My
suspicion
Richard O'Keefe wrote:
There's something in that package that I don't understand,
and I feel really stupid about this.
data RVarT m a
type RVar = RVarT Identity
class Distribution d t where
rvar :: d t - RVar t
rvarT :: d t - RVarT n t
Where does n come from?
Presumably from
There's something in that package that I don't understand,
and I feel really stupid about this.
data RVarT m a
type RVar = RVarT Identity
class Distribution d t where
rvar :: d t - RVar t
rvarT :: d t - RVarT n t
Where does n come from?
There's no reason to feel stupid when
On Jun 3, 2010, at 6:40 PM, mo...@deepbondi.net wrote:
If anyone knows a way this could be done while still allowing general
functions to be mapped over RVars, I'd love to hear about it. My
suspicion though is that it is not possible. It would be a very
similar
problem to computing the
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