There are 4 repositories under gibbs-sampling topic.
Boltzmann Machines in TensorFlow with examples
Collection of probabilistic models and inference algorithms
Open Source Package for Gibbs Sampling of LDA
Improving topic models LDA and DMM (one-topic-per-document model for short texts) with word embeddings (TACL 2015)
Implement of L-LDA Model(Labeled Latent Dirichlet Allocation Model) with python
A PureScript, browser-based implementation of LDA topic modeling.
A method for variant graph genotyping based on exact alignment of k-mers
A Java package for the LDA and DMM topic models
Bayesian Factorization with Side Information in C++ with Python wrapper
An unsupervised machine learning algorithm for the segmentation of spatial data sets.
Explaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.
David Mackay's book review and problem solvings and own python codes, mathematica files
Clone identification from single-cell data
Inference in Bayesian Belief Networks using Probability Propagation in Trees of Clusters (PPTC) and Gibbs sampling
AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.
Functions for Bayesian inference of vector autoregressive and vector error correction models
Matlab implementations of various multi-sensor labelled multi-Bernoulli filters
Blazing fast topic modelling for short texts.
GSDMM: Short text clustering (Rust implementation)
Visualization of Gibbs sampling for 2D Gaussian distribution
CorBinian: A toolbox for modelling and simulating high-dimensional binary and count-data with correlations
Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python
A toolbox for inference of mixture models
Low-level primitives for collapsed Gibbs sampling in python and C++
social Discrete Choice Models in Python
Python implementation of Restricted Boltzmann Machine (RBM). And an example on MNIST dataset.
A Unified RNA Sequencing Model (URSM) for joint analysis of single cell and bulk RNA-seq data.
Accurate estimation of conditional categorical probability distributions using Hierarchical Dirichlet Processes
Bayesian Statistics with R [Gibbs Sampling, Metrapolis Hastings, Regression, Logistic Regression, Poisson Regression, Multi Factor Anova, Hierarchical Modelling, Mixture Models]
Gibbs sampler for the Distance Dependent Chinese Restaurant Process