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How might this be combined with a training/testing data splitting paradigm, or even using an ML model?
by antipaul 6y ago
How might this be combined with a training/testing data splitting paradigm, or even using an ML model?
- causalmodels 6y agoProbabilistic machine learning is still machine learning. If you're referring to one of the popular tensor frameworks, pretty much all of them have probabilistic features or add-ons [1][2][3]. There are also frameworks developed specifically for this kind of work [4][5]. This [6] is a more in depth look at hierarchical / multi-level modeling. The prediction section [7] specifically goes over cross validation and inference. [1] https://www.tensorflow.org/probability/examples/Multilevel_Modeling_Primer https://www.tensorflow.org/probability/examples/Multilevel_M... [2] https://pyro.ai/examples/forecasting_iii.html https://pyro.ai/examples/forecasting_iii.html [3] http://edwardlib.org/ http://edwardlib.org/ [4] https://mc-stan.org/ https://mc-stan.org/ [5] https://docs.pymc.io/ https://docs.pymc.io/ [6] https://docs.pymc.io/notebooks/multilevel_modeling.html https://docs.pymc.io/notebooks/multilevel_modeling.html [7] https://docs.pymc.io/notebooks/multilevel_modeling.html#Prediction https://docs.pymc.io/notebooks/multilevel_modeling.html#Pred...
- RA_Fisher 6y agoML are algos not models (estimators to be precise). This model could be fit by many of them. However, fitting the model directly with the linear model will be advantageous for computational reasons.