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
A multiverse of Prophet models for timeseries
Efficient library for spectral analysis in high-energy astrophysics.
Novel technique to fit a target distribution with a class of distributions using SVI (via NumPyro). Unlike standard SVI, our "data" is a distribution rather than a finite collection of samples.
Bayesian inference using sparse gaussian processes from tinygp. Examples include 1D and 2D implementation.
Summary notebook implementing Bayesian Model Averaging with 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..
Build, fit, and sample from cognitive models with JAX + NumPyro.
Mixture regression models for NumPyro.
My implementation of John K. Kruschke's Doing Bayesian Data Analysis 2nd edition using Python and Numpyro.
Pretty, easy, flexible Bayesian estimation with data overlay
Probabilistic deep learning using JAX
Estimating time trees from very large phylogenies
Statistical rethinking by Richard McElreath. Learning notes, code port to PyMC (mainly for MCMC) v5 & Numpyro (mainly for `quap`).
Bayesian Learning and Neural Networks (jupyter book sources)
Scalable Bayesian Modelling: A comparison
Tutorials for the 2022 IAIFI Summer School, covering (deep) probabilistic programming with Jax and NumPyro.
Bayesian Analysis in Python (2nd ed.) with Numpyro
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