getMargin for Dependent MultiNormal?

[jacobstanley]

Is it possible to marginalize a variable which is part of a dependent multi-normal distribution? Not sure if that is the right terminology, I'm new to machine learning.

I basically have this:

data MyData = MyData
    { _foo :: String
    , _bar :: String
    , _fooN :: Double
    , _barN :: Double
    }

type MyDist = Multivariate MyData
    '[ MultiCategorical '[String, String]
     , Dependent MultiNormal '[Double, Double]
     ]
     Double

And I get a type error when I try to do this:

-- dist :: MyDist
getMargin TH_fooN $ condition TH_foo "foo" $ condition TH_bar "bar" dist

Am I supposed to be able to do that or is there another way I can sample the distribution? Ideally I want to get a tuple with the value for fooN and barN together given the specified conditions.

[mikeizbicki]

Yes, this is easy to do in principle. It looks like I never actually implemented it though for some reason, and that's why the type error occurs.

I probably won't be adding this feature any time soon. Working with these types has proven too cumbersome, and so the latest version on github completely scraps this syntax. I have yet to come up with a better one :(

[jacobstanley]

Would this configuration be possible with the latest version on github? I'm already using master from github, I don't mind switching to a different branch if it's better equip to deal with my situation.

I don't mind which syntax I use :)

[jacobstanley]

If you're saying that it won't be implemented any time soon because you have yet to come up with a better syntax, then... well damn :( haha

[mikeizbicki]

The latest dev branch uses some features from ghc 7.8. This upgrade broke all the code for multivariate distributions, and I haven't done anything to fix it yet.

It would be possible to add this capability to the master branch on github. It would involve making the MultiNormal type (https://github.com/mikeizbicki/HLearn/blob/master/src/HLearn/Models/Distributions/Multivariate/MultiNormal.hs) implement the Marginalize class (https://github.com/mikeizbicki/HLearn/blob/master/src/HLearn/Models/Distributions/Multivariate/Internal/Marginalization.hs).

In principle this is super easy. All you do is extract the right variance term from the covariance matrix (along the diagonal) and plug it into the Normal class. In practice, though, I'm not sure how easy this will be to make the types line up right.

If you're up for a challenge and want to implement it, I'd happily merge the code :)

[jacobstanley]

I'll give it a shot, I suspect that if I can get the types to line up you can easily tell me if I'm screwing up the variance extraction

[jacobstanley]

I just want to check, do I need to implement the Marginalize class or the Marginalize' class?

[mikeizbicki]

Marginalize', you're right. I can definitely help you out with any questions like that.