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Linear combinations of Expectation of EmpiricalDistribution
*To*: mathgroup at smc.vnet.net
*Subject*: [mg128132] Linear combinations of Expectation of EmpiricalDistribution
*From*: Clemens Fruhwirth <clemens at endorphin.org>
*Date*: Tue, 18 Sep 2012 03:40:19 -0400 (EDT)
*Delivered-to*: l-mathgroup@mail-archive0.wolfram.com
*Delivered-to*: l-mathgroup@wolfram.com
*Delivered-to*: mathgroup-newout@smc.vnet.net
*Delivered-to*: mathgroup-newsend@smc.vnet.net
Dear MathGroup,
The expectation for any linear combination of random values is the
linear combination of the respective expectations of the random
values, such as
E(aX+bY) = aE(X) + bE(Y)
I wonder why Mathematica can't resolve this rule when
EmpiricalDistributions come into play. For instance,
Expectation[
x + y, {x \[Distributed] EmpiricalDistribution[{0, 1, 2}],
y \[Distributed] EmpiricalDistribution[{0, 10, 20}]}]
I came up with:
ExpectationX[
a_ + b_, {x_ \[Distributed] xdist_, y_ \[Distributed] ydist_}] :=
a + b //. {x -> Expectation[x, x \[Distributed] xdist],
y -> Expectation[y, y \[Distributed] ydist]}
to resolve at least simple addition. But before I put more work into that:
* Am I missing an assumption here or some syntax? Or is this rule just
not built into Mathematica?
* Is that in general the right approach to extend Mathematica?
Thanks!
--
Fruhwirth Clemens http://clemens.endorphin.org
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