Compatibility probability of measurements across experiments
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Updated
Jun 13, 2017 - MATLAB
Compatibility probability of measurements across experiments
A web app for analyzing A/B testing data using Bayesian approach
Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
Gauss Naive Bayes in Python From Scratch.
Code relative to paper arXiv:1808.01930 [hep-ex]
Markov Chain Monte Carlo on graph space applied to the study of neuronal interactions from experimental data
Kronecker-product-based linear inversion under Gaussian and separability assumptions.
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.
Bayesian Inference
daubl: Digit analysis using Benford's law
pcal: Calibration of p-values for point null hypotheses
A curated list of my Artificial Intelligence project.
Bayesian Logistic Regression with Python and PyMC3 to predict customer subscription for a financial institution.
Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.
Tools for the Bayesian Discount Prior Function
🚫 ↩️ A document that introduces Bayesian data analysis.
Histogram based classification and prediction of annual rainfall from Kerala dataset
The Plausible Parameter Space (PPS) Shiny App is designed to help users define their priors in a linear regression with two regression coefficients.
Kronecker-product-based linear inversion under Gaussian and separability assumptions.
Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.
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