Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model
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Updated
Dec 8, 2022 - Jupyter Notebook
Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model
A Python/C++ implementation of Bayesian Factorization Machines
Hierarchical, multi-label topic modelling with LDA
A Latent Dirichlet Allocation implementation in Python.
Visualization of Gibbs sampling for 2D Gaussian distribution
Code to perform multivariate linear regression using Gibbs sampling
Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.
glmdisc Python package: discretization, factor level grouping, interaction discovery for logistic regression
Generating samples from Ising model.
Bayesian trend filtering micro library. http://trendpy.readthedocs.io/en/latest/
R implementation of the Dirichlet Process Gaussian Mixture Model (with MCMC)
Libary for SGPD (Sigmoidal Gaussian Process Density) inference
Approximate Bayesian inference for mixed effects models with heterogeneity
In the first semester of my MSc. studies, we developed a phyton version of the Gibbs Sampler and Metropolis-Hastings Algorithm from the scratch. We described our results and analysis in a report.
Localization of brain sources measured by EEG using 3 approaches: Gibbs sampler (Markov chain Monte Carlo algorithm), Minimum Norm Estimates (MNE) and Source Imaging based on Structured Sparsity (SISSY)
Bayesian Regression Analyses from scratch - NBA data example
Package to do Bayesian inference with Gibbs sampler
Implementation of a Gibbs-Metropolis sampling algorithm in CUDA
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