Re: Small Markov models problem

Ross Clement (Email address invalid - do not use) wrote:
Now, my real problem is this: My actual models are two extremes. One
is calculated with an independence assumption between two variables
in
underlying details which I haven't explained. The second is
calculated
with an assumption that these same two variables are equivalent. I
would like to claim that the "intermediate" models show varying
degrees of dependence between the two variables, from 0 (full
independence) to
1.0 (full dependence/equivalence).


I think that most modellers would want to work with the conditional
probabilities and to use these to judge whether a model showed more or
less dependence than another. However, working with conditional
probabilities leaves the extra step of having to fix-up the marginal
distibutions to remain the same. For your model (treating the 0.3, 0.7
weights a derived from a parameter), can you derive the conditional
probabilities and show that that the parameter has the effect of
indicating more or less dependence in terms of the conditional
probabilities?

David Jones


.

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