Bayesian Statistics MOOC by Coursera - Solutions in Python
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Updated
Feb 4, 2024 - Jupyter Notebook
Bayesian Statistics MOOC by Coursera - Solutions in Python
An R package for clustering longitudinal datasets in a standardized way, providing interfaces to various R packages for longitudinal clustering, and facilitating the rapid implementation and evaluation of new methods
Tools for Analyzing Finite Mixture Models
A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.
A toolbox for inference of mixture models
This project contains the code for the paper accepted at NeurIPS 2020 - Robust Meta-learning for Mixed Linear Regression with Small Batches.
Tidy Tools for Visualizing Mixture Models
Clustering and segmentation of heterogeneous functional data (sequential data) with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm
News Article Clustering Using Unsupervised Machine Learning Algorithms
Trinomial mixture models in Stan, for fitting to compositional data with 0s
Model-based clustering with vine copulas
CRAN Task View: Cluster Analysis & Finite Mixture Models
GH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).
A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.
Code supplement for "Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction"
A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials, developed by various projects of the University of Twente (NL), the Netherlands eScience Center (NL), University of Newcastle (AU), and Hiroshima University (JP).
Functional Latent datA Models for clusterING heterogeneOus curveS
R Package That Can Simultaneously Perform Factor Analysis And Cluster Analysis Of Count Data Via Parsimonious Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers. This Model Permits For Parsimonious Covariance Structures And Dimension Reduction, Thus Reducing The Number Of Free Parameters To Be Calculated.
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