Matrix Determinant Toolkit
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Updated
Jul 22, 2024 - Python
Matrix Determinant Toolkit
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.
Histogram based classification and prediction of annual rainfall from Kerala dataset
🚫 ↩️ A document that introduces Bayesian data analysis.
Tools for the Bayesian Discount Prior Function
Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.
Bayesian Logistic Regression with Python and PyMC3 to predict customer subscription for a financial institution.
A curated list of my Artificial Intelligence project.
pcal: Calibration of p-values for point null hypotheses
daubl: Digit analysis using Benford's law
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.
Kronecker-product-based linear inversion under Gaussian and separability assumptions.
Markov Chain Monte Carlo on graph space applied to the study of neuronal interactions from experimental data
Code relative to paper arXiv:1808.01930 [hep-ex]
Gauss Naive Bayes in Python From Scratch.
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