Use Python and JAX to efficiently build infinitely wide networks for deeper network insights on your finite machine

What happens when your neural networks stretch into infinity. Image: ESA/Hubble & NASA,


Efficiently exploring the parameter-search through Bayesian Optimization with skopt in Python. TL;DR: my hyperparameters are always better than yours.

Explore vast canyons of the problem space efficiently — Photo by Fineas Anton on Unsplash

About Hyperparameters


A simple, yet meaningful probabilistic Pyro model to uncover change-points over time.

Solitude. A photo by Sasha Freemind on Unsplash

Making an assumption tangible


Make the best of missing data the Bayesian way. Improve model performance and make comparative benchmarks using Monte Carlo methods.

A Missing frame ready to throw you off your model. Photo by Vilmos Heim on Unsplash.

The Ugly Data


Build better Data Science workflows with probabilistic programming languages and counter the shortcomings of classical ML.

The tools to build, train and tune your probabilistic models. Photo by Patryk Grądys on Unsplash.

Classical ML workflows are missing something

  1. Have a use-case or research question with a potential hypothesis,
  2. build and curate a dataset that relates to the use-case or research question,
  3. build a model,
  4. train and validate the model,
  5. maybe even cross-validate, while grid-searching hyper-parameters,
  6. test the fitted model,
  7. deploy the model for the use-case,
  8. answer the research question or hypothesis you posed.


Modeling U.S. cancer-death rates with two Bayesian approaches: MCMC in STAN and SVI in Pyro.

Modeling death-rates across U.S. counties — Photo by Joey Csunyo on Unsplash

Kidney Cancer Data


The way from data novice to professional

Clock with reverse numeral, Jewish Town-hall Clock, Prague - by Richard Michael (all rights reserved).


One reason why Bayesian Modeling works with real world data. The approximate light-house in the sea of randomness.

Photo by William Bout on Unsplash


The answer to: “Why is my model running forever?” or the classic: “I think it might have converged?”


Connect the dots over time and forecast with confidence(-intervals).

Connecting dots across time — photo by israel palacio on Unsplash

Richard Michael

I am a Data Scientist and M.Sc. student in Bioinformatics at the University of Copenhagen. You can find more content on my weekly blog http://laplaceml.com/blog

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