Hi, Jimmy,
Try this: Run your model, including the correlations using 20,000 or
more iterations. Extract trial values for F, X, Y, Z. Sort the data on
X. Locate a subset of the data whose X-values are, say, + or – 1%
from the desired X-value. Sort the reduced data set on Y and locate a
subset of this data whose Y-values are "close" to the desired value of
Y. Plot F against Z.
You can see from this experiment that you will not have any values in
the database where the X and Y values are exactly what you specify.
Depending on your distributions and the functional relationship, you
might get close enough for your purposes. I tried the experiment with
a couple of functions and got very different results. Had X and Y been
discrete variables, you might have found some exact matches.
Regards,
Jim

--- In cbug2@yahoogroups.com, "asujames" <asujames@...> wrote:
>
> The distribution are continious gamma functions.
>
> I do not get what you mean by
>
> "If X and Y are continuous, then the probability of their assuming
> particular fixed values is of course 0"
>
> How does that help me ... I'm sure it does I just do see it
>
> Thanks
>
>
>
>
> --- In cbug2@yahoogroups.com, "jamesamurtha" <jmurtha@> wrote:
> >
> > Hi, Jimmy,
> > If X and Y are continuous, then the probability of their assuming
> > particular fixed values is of course 0.
> > If X and Y are discrete, then you can run the simulation with lots of
> > iterations (depending on the number of possible combinations of values
> > of X and Y), extract the simulation data, and sort on X then Y. Simple
> > counting will allow you to calculate the desired conditional
> probability.
> > Regards,
> > Jim
> >
> > --- In cbug2@yahoogroups.com, "asujames" <asujames@> wrote:
> > >
> > > Hello all,
> > >
> > > I'm trying to model conditional proabailities in CB. Quick
> > > desscribtion of my problem.
> > >
> > > I have three distributions as inputs (X, Y, Z) and I run my MC
to get
> > > output F. There is a correlation between X and Z and Y and Z. I
want
> > > to be able to specify X and Y and run my simulation, but at the
same
> > > time I want the correleation between the distributiuons to remain.
> > > Because of this I can't just set X and Y to a value and run because
> > > CB defines the correlations to assumptions.
> > >
> > > What I have done. I tried to use the 2D simulation tool to perform
> > > thsi task, which seemed liek it shodul work because it will set
X and
> > > Y and then run the MC for Z and forcast my output F. The porbelm is
> > > the corrleatiosn do not remain between X and Z or Y and Z. So I can
> > > get a conditional probability for F but it doesn't include a
> > > crrelation ebwteen my inputs.
> > >
> > > The goals is to determin the 99th confidence for F given a X and Y,
> > > and running MC all all of my other inputs in thsi case just Z and
> > > keeping the correlations between my inputs.
> > >
> > > Has anyone tried to answer a simular type of problem and can
help or
> > > maybe you see a solution that I'm missing
> > >
> > > Thanks
> > >
> > > jimmy
> > >
> >
>