Probabilistic ML/DL

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Adding uncertainty estimates to Keras models with tfprobability

Artificial Intelligence, Concepts, Probabilistic ML/DL, TensorFlow/Keras

As of today, there is no mainstream road to obtaining uncertainty estimates from neural networks. All that can be said is that, normally, approaches tend to be Bayesian in spirit, involving some way of putting a prior over model weights. This holds true as well for the method presented in this post: We show how […]

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Dynamic linear models with tfprobability

Artificial Intelligence, Probabilistic ML/DL, Time Series

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

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