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On leapfrogs, crashing satellites, and going nuts: A very first conceptual introduction to Hamiltonian Monte Carlo

On leapfrogs, crashing satellites, and going nuts: A very first conceptual introduction to Hamiltonian Monte Carlo

TensorFlow Probability, and its R wrapper tfprobability, provide Markov Chain Monte Carlo (MCMC) methods that were used in a number of recent posts on this blog. These posts were directed to users already comfortable with the method, and terminology, per se, which readers mainly interested in deep learning won’t necessarily be. Here we try to make up leeway, introducing Hamitonian Monte Carlo (HMC) as well as a few often-heard “buzzwords” accompanying it, always striving to keep in mind what it is all “for”

Sigrid Keydana

Oct 3, 2019

tfprobability Best Practices Ai Bayesian Modeling Concepts

blog post

Modeling censored data with tfprobability

Modeling censored data with tfprobability

In this post we use tfprobability, the R interface to TensorFlow Probability, to model censored data. Again, the exposition is inspired by the treatment of this topic in Richard McElreath’s Statistical Rethinking. Instead of cute cats though, we model immaterial entities from the cold world of technology: This post explores durations of CRAN package checks, a dataset that comes with Max Kuhn’s parsnip

Sigrid Keydana

Jul 31, 2019

keras3 Machine Learning Ai Bayesian Modeling TensorFlow Keras

blog post

Hierarchical partial pooling, continued: Varying slopes models with TensorFlow Probability

Hierarchical partial pooling, continued: Varying slopes models with TensorFlow Probability

This post builds on our recent introduction to multi-level modeling with tfprobability, the R wrapper to TensorFlow Probability. We show how to pool not just mean values (“intercepts”), but also relationships (“slopes”), thus enabling models to learn from data in an even broader way. Again, we use an example from Richard McElreath’s “Statistical Rethinking”; the terminology as well as the way we present this topic are largely owed to this book

Sigrid Keydana

May 24, 2019

keras3 Machine Learning Ai Bayesian Modeling TensorFlow Keras

blog post

Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability

Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability

This post is a first introduction to MCMC modeling with tfprobability, the R interface to TensorFlow Probability (TFP). Our example is a multi-level model describing tadpole mortality, which may be known to the reader from Richard McElreath’s wonderful “Statistical Rethinking”

Sigrid Keydana

May 6, 2019

keras3 Machine Learning Ai Bayesian Modeling TensorFlow Keras