Reconstructing a black and white Japanese woodblock print using Bayesian inference
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Updated
Jan 5, 2024 - Jupyter Notebook
Reconstructing a black and white Japanese woodblock print using Bayesian inference
The goal of rrum is to provide an implementation of Gibbs sampling algorithm for Bayesian Estimation of reduced Reparametrized Unifed Model (rRUM), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
In this project, we developed three ML models to do parts of speech tagging.
Gibbs Sampling & EM Algorithm Implementation / R Programming Language / on Iris Dataset
Implementation of Gibbs sampling. 1. Gamma-Poisson mixture model for topic modeling 2. Bernolli-Beta Mixture model
The inspections on some important literatures, mainly including codes.
Bayesian variable selection method for finite mixture model of linear regressions
Motif Finding using Gibbs Sampler
Notebooks for Bayesian Foundations (Course 1)
Inclusion by Design Project
Investigating the efficacy of diagnostic kits used for parasitic disease surveillance in the Philippines.
Package to do Bayesian inference with Gibbs sampler
Inference in Bayesian Belief Networks using Probability Propagation in Trees of Clusters (PPTC) and Gibbs sampling
Graph: Representation, Learning, and Inference Methods
A Julia package for bayesian probabilistic matrix factorization (BPMF).
Gibbs Sampling Dirichlet Multinomial Model (GSDMM) for Short-Text Clustering
A Python/C++ implementation of Bayesian Factorization Machines
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