stat/distmv: Add ScoreInput function for the Normal distribution #103
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Fixed a bug in the implementation. Very tricksy. |
stat/distmv/normal.go
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| @@ -277,6 +277,38 @@ func (n *Normal) Rand(x []float64) []float64 { | |||
| return x | |||
| } | |||
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| // ScoreInput returns the score function with respect to the input of the | |||
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I would rewrite these two sentences as: ScoreInput returns the value at x of the score function s which is defined as the gradient of the log-probability: s(x) = \nabla log(p(x)).
If gradient is not used in statistics, then keep derivative.
Also, why not call the method just Score?
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Because the score function https://en.wikipedia.org/wiki/Score_(statistics) is the derivative with respect to the parameters, and this is with respect to the inputs.
stat/distmv/normal.go
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| // If deriv is nil, a new slice will be allocated and returned. If deriv is of | ||
| // length the dimension of Normal, then the result will be put in-place into deriv. | ||
| // If neither of these is true, ScoreInput will panic. | ||
| func (n *Normal) ScoreInput(deriv, x []float64) []float64 { |
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I would call the first parameter score.
stat/distmv/normal.go
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| // input x. That is, ScoreInput computes | ||
| // ∇_x log(p(x)) | ||
| // If score is nil, a new slice will be allocated and returned. If score is of | ||
| // length the dimension of Normal, then the result will be put in-place into deriv. |
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deriv does not exist. Do you mean score?
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Yes, sorry, missed that one.
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Done. (sorry for squashing, made some silly git decisions)
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This has gone semantically stale. It uses the solve method on |
stat/distmv/normal.go
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| dv := mat.NewVector(len(score), score) | ||
| dt := mat.NewVector(len(tmp), tmp) | ||
| dv.SolveCholeskyVec(&n.chol, dt) |
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n.chol.SolveVec(mat.NewVector(len(score), score), mat.NewVector(len(tmp), tmp))
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Rebased. |
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