🚫 ↩️ 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.

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

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

Bayesian Inference

A curated list of my Artificial Intelligence project.

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

Computational framework for Mean-Field Game-based optimal execution with latent market dynamics, endogenous price impact, posterior filtering, and heterogeneous agent equilibrium interactions.

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

Matrix Determinant Toolkit (memdet)

Tools for the Bayesian Discount Prior Function

Compatibility probability of measurements across experiments

daubl: Digit analysis using Benford's law

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