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Estimating Public Market Exposure of Private Capital Funds Using Bayesian Inference

I don’t know anything about this work by Luis O’Shea and Vishv Jeet—that is, I know nothing of public market exposure or private capital firms, and I don’t know anything about the model they fit, the data they used, or what information they had available for constructing and checking their model.

But what I do know is that they fit their model in Stan.

Fitting models in Stan is just great, for the usual reasons of flexible modeling and fast computing, and also because Stan code can be shared, so we—the Stan user community and the larger research community—can learn from each other and move all our data analyses forward.


  1. Paul says:

    I agree, Stan is great, especially in terms of flexibility. However, when it comes to computation time I often find it of limited use for my daily work. If I’m able to formulate all my effects as Gaussian Markov random fields (which is most often the case) I prefer the INLA approach which gives me highly accurate results within seconds rather than minutes or hours.

    So yeah, Stan is great, really. But in terms of speed there is room for improvement.

  2. Thomas says:

    Have the authors treated this time series data as iid – those intervals (whatever they are) around the expected mean look pretty narrow?

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