Python code for "Probabilistic Machine learning" book by Kevin Murphy
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Updated
Jun 13, 2024 - Jupyter Notebook
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Bayesian Learning and Neural Networks (jupyter book sources)
Estimating time trees from very large phylogenies
A multiverse of Prophet models for timeseries
Probabilistic deep learning using JAX
Efficient library for spectral analysis in high-energy astrophysics.
Scalable Bayesian Modelling: A comparison
My implementation of John K. Kruschke's Doing Bayesian Data Analysis 2nd edition using Python and Numpyro.
Tutorials for the 2022 IAIFI Summer School, covering (deep) probabilistic programming with Jax and NumPyro.
Summary notebooks using derivative gaussian processes with tinygp. We implement a 2D derivative gaussian process and successfully use derivatives to regularize SVI fits with a gaussian process model..
Very easy Bayesian regression.
Bayesian inference using sparse gaussian processes from tinygp. Examples include 1D and 2D implementation.
Repo for course CSC2558: "Intelligent Adaptive Interventions" project in nonstationary contextual bandits.
Mixture regression models for NumPyro.
Statistical rethinking by Richard McElreath. Learning notes, code port to PyMC (mainly for MCMC) v5 & Numpyro (mainly for `quap`).
Build, fit, and sample from cognitive models with JAX + NumPyro.
Summary notebook implementing Bayesian Model Averaging with numpyro.
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