There are 1 repository under mixture-model topic.
Implementation of "Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image"
Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.
An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology - CVPR 2024
Implementation of Switch Transformers from the paper: "Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity"
An unsupervised machine learning algorithm for the segmentation of spatial data sets.
Bayesian inference for Gaussian mixture model with some novel algorithms
Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
Model-based subclonal deconvolution from bulk sequencing.
:white_medium_small_square: <- :white_circle: Structural Equation Modeling from a broader context.
Mixture of experts on convolutional neural network using Keras and Cifar10
Some recent state-of-the-art generative models in ONE notebook: (MIX-)?(GAN|WGAN|BigGAN|MHingeGAN|AMGAN|StyleGAN|StyleGAN2)(\+ADA|\+CR|\+EMA|\+GP|\+R1|\+SA|\+SN)*
PyTorch implementation of the mixture distribution family with implicit reparametrisation gradients.
R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model
My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin
Model-based clustering package for mixed data
An HMM and Phylogenetic Placement based Ultra-Fast Taxonomy Assignment Tool for 16S sequencing
Python (pip) package for fitting mixtures of Student's t-distributions using either maximum likelihood (EM) or Bayesian methodology (variational mean-field)
Bayesian Statistics with R [Gibbs Sampling, Metrapolis Hastings, Regression, Logistic Regression, Poisson Regression, Multi Factor Anova, Hierarchical Modelling, Mixture Models]
Herramientas estadísticas para la investigación
The code for fitting a mixture distribution to data and Gaussian Mixture Model (GMM)
A Fast and Simplified Python Library for Uncertainty Estimation
Multimodal Exponentially Modified Gaussians with Optional Oscillation
Plackett-Luce Regression Mixture Model
The goal of ‘aldvmm’ is to fit adjusted limited dependent variable mixture models of health state utilities in R. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package ‘aldvmm’ uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) using normal component distributions and a multinomial logit model of probabilities of component membership.