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function StatsBase.informationmatrix( |
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m::ModeResult; hessian_function=ForwardDiff.hessian, kwargs... |
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) |
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# This needs to be calculated in unlinked space |
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model = m.f.ldf.model |
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vi = DynamicPPL.VarInfo(model) |
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getlogdensity = _choose_getlogdensity(m.estimator) |
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new_optimld = OptimLogDensity(DynamicPPL.LogDensityFunction(model, getlogdensity, vi)) |
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|
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# Calculate the Hessian, which is the information matrix because the negative of the log |
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# likelihood was optimized |
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varnames = StatsBase.coefnames(m) |
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info = hessian_function(new_optimld, m.values.array[:, 1]) |
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return NamedArrays.NamedArray(info, (varnames, varnames)) |
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end |
this should take an adtype rather than a hessian function
