On Wed, May 25, 2016 at 1:13 PM, S Ellison <s.elli...@lgcgroup.com> wrote:
> > -----Original Message-----
> > My data come from statistical model N(5, 2), with n=100, call this
> model_1
> > Then, I add bias to that data with N(3, 1), with n=100, call this model_2
> Do you mean you have data from N(5,2) that has had data from N(3,1) added
> to it, or that you have two different sets of data?
> Or do you mean that you want to know how to generate such data?
>
I generate toy data from N(5,2). X input will be the same for model_1 and
model_2, say, seq(-3, 3, by=0.01).
> > Ultimately, I want to see model_1+ model_2 gives good prediction
> If you generate random data correctly following a model, the model will
> indeed predict the data pretty well. But under those circumstances it seems
> redundant to ask the question. Were you thinking of fitting a (possibly
> different) model to the data at some point ? If so, what model would you
> want to fit? And what would you want to predict from it?
>
I only use model_1, model_2 for data generation. I am using machine
learning methods for estimation and prediction to see if the method is good
and robust. Then, check performance by comparing results with the known
model_1 and model_2.
> > or perhaps parameter estimation.
> What parameters do you want to estimate?
>
> > I think this is a pretty standard statistical analysis problem?
> Unclear on that at present. See above.
>
>
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