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  "Title": "Create Mixture Models From Predictive Samples",
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  "Description": "Combines predictions from individual time series or panel\ndata models into an ensemble using stacking (Yao, Vehtari,\nSimpson, and Gelman (2018) <doi:10.1214/17-BA1091>) based on\nthe Continuous Ranked Probability Score (CRPS) (Gneiting and\nRaftery (2007) <doi:10.1198/016214506000001437>) over k-step\nahead predictions. Predictions must be predictive distributions\nrepresented by samples, typically posterior predictive\nsimulation draws from a Markov chain Monte Carlo (MCMC)\nalgorithm. Given training data with observed values and\npredictive samples from different models, optimal stacking\nweights are computed to minimize expected cross-validation\npredictive error. These weights can then be used to generate\nsamples from the mixture model by drawing from individual model\npredictions in the correct proportions.",
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