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Jun 9, 2023
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Update loss.md #133

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4 changes: 2 additions & 2 deletions docs/src/developer/loss.md
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Original file line number Diff line number Diff line change
Expand Up @@ -217,7 +217,7 @@ Let's make a sligtly more complicated example: we will reimplement maximum likel
To keep it simple, we only cover models without a meanstructure. The maximum likelihood objective is defined as

```math
F_{ML} = \log \det \Sigma_i + \mathrm{tr}(\Sigma_i \Sigma_o)
F_{ML} = \log \det \Sigma_i + \mathrm{tr}\left(\Sigma_{i}^{-1} \Sigma_o \right)
```

where ``\Sigma_i`` is the model implied covariance matrix and ``\Sigma_o`` is the observed covariance matrix. We can query the model implied covariance matrix from the `imply` par of our model, and the observed covariance matrix from the `observed` path of our model.
Expand Down Expand Up @@ -269,4 +269,4 @@ model_ml = SemFiniteDiff(
model_fit = sem_fit(model_ml)
```

If you want to differentiate your own loss functions via automatic differentiation, check out the [AutoDiffSEM](https://github.com/StructuralEquationModels/AutoDiffSEM) package (spoiler allert: it's really easy).
If you want to differentiate your own loss functions via automatic differentiation, check out the [AutoDiffSEM](https://github.com/StructuralEquationModels/AutoDiffSEM) package (spoiler allert: it's really easy).