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 Latent Dirichlet Allocation implementation in Python.
Hierarchical, multi-label topic modelling with LDA
Visualization of Gibbs sampling for 2D Gaussian distribution
A Python/C++ implementation of Bayesian Factorization Machines
Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.
Code to perform multivariate linear regression using Gibbs sampling
Implementation of a Gibbs-Metropolis sampling algorithm in CUDA
Latent space competing risk model for response and response time analysis
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
Motif Finding using Gibbs Sampler
glmdisc Python package: discretization, factor level grouping, interaction discovery for logistic regression
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.
Bayesian trend filtering micro library. http://trendpy.readthedocs.io/en/latest/
statistical modelling of the wine data-set available at https://www.kaggle.com/zynicide/wine-reviews
Generating samples from Ising model.
Instructed by : Prof. Manisha Pal. A repository created with the practical problems on Bayesian computing and some advance computing related to MCMC, Metropolis etc.
Bayesian Mixture Model applied to cluster analysis of breast tumours using MCMC.
R implementation of the Dirichlet Process Gaussian Mixture Model (with MCMC)
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