🚫 ↩️ A document that introduces Bayesian data analysis.

Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch

Gauss Naive Bayes in Python From Scratch.

causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.

R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.

Kronecker-product-based linear inversion under Gaussian and separability assumptions.

Kronecker-product-based linear inversion under Gaussian and separability assumptions.

The Plausible Parameter Space (PPS) Shiny App is designed to help users define their priors in a linear regression with two regression coefficients.

Bayesian Inference

AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs.

A web app for analyzing A/B testing data using Bayesian approach

Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.

Code relative to paper arXiv:1808.01930 [hep-ex]

A curated list of my Artificial Intelligence project.

daubl: Digit analysis using Benford's law

Bayesian Logistic Regression with Python and PyMC3 to predict customer subscription for a financial institution.

Compatibility probability of measurements across experiments

Markov Chain Monte Carlo on graph space applied to the study of neuronal interactions from experimental data

pcal: Calibration of p-values for point null hypotheses