Dynamic linear models with tfprobability
This article is originally published at https://blogs.rstudio.com/tensorflow/Previous posts featuring tfprobability - the R interface to TensorFlow Probability - have focused on enhancements to deep neural networks (e.g., introducing Bayesian uncertainty estimates) and fitting hierarchical models with Hamiltonian Monte Carlo. This time, we show how to fit time series using dynamic linear models (DLMs), yielding posterior predictive forecasts as well as the smoothed and filtered estimates from the Kálmán filter.
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This article is originally published at https://blogs.rstudio.com/tensorflow/
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