Labs for University course
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Updated
Jul 11, 2024 - Python
Labs for University course
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)
Here you can find several cpp projects i've done while im learning this programming language.
Missing data imputation using the exact conditional likelihood of Deep Latent Variable Models
Project done as part of the course on Bayesian Statistics, Anna Simoni. Implementation of a block Gibbs sampler.
Solutions for Biology Meets Programming: Bioinformatics for Begineers course in Coursera
Reconstructing a black and white Japanese woodblock print using Bayesian inference
Instructed by : Prof. Manisha Pal. A repository created with the practical problems on Bayesian computing and some advance computing related to MCMC, Metropolis etc.
glmdisc Python package: discretization, factor level grouping, interaction discovery for logistic regression
A Python/C++ implementation of Bayesian Factorization Machines
Bayesian Regression Analyses from scratch - NBA data example
Latent space competing risk model for response and response time analysis
Bayesian Mixture Model applied to cluster analysis of breast tumours using MCMC.
Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model
Hierarchical, multi-label topic modelling with LDA
Motif Finding using Gibbs Sampler
Code to perform multivariate linear regression using Gibbs sampling
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.
R package bayessource: marginal likelihood and Bayes Factor computation for samples from Multivariate Gaussians
Rmarkdowns with code & commentary on implementing my first gibbs sampler.
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