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Search results for mixture model
mixture-model
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34 search results found
Lifting From The Deep Release
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391
Implementation of "Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image"
Spflow
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272
Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks
Miscellaneous R Code
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140
Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. Now almost entirely superseded by the models-by-example repo.
Iq Tree
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125
Efficient phylogenomic software by maximum likelihood
Libcluster
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119
An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.
Pybgmm
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49
Bayesian inference for Gaussian mixture model with some novel algorithms
Bayseg
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35
An unsupervised machine learning algorithm for the segmentation of spatial data sets.
Stepmix
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35
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.
Dpmmsubclusters.jl
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30
Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
Mobster
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29
Model-based subclonal deconvolution from bulk sequencing.
Latrend
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22
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
Coursera Uw Machine Learning Clustering Retrieval
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21
Plotmm
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21
Tidy Tools for Visualizing Mixture Models
Sem
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15
◽ <- ⚪ Structural Equation Modeling from a broader context.
Mplnclust
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15
R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model
Expectationmaximization.jl
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14
A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.
Mixtools
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14
Tools for Analyzing Finite Mixture Models
Torch Reparametrised Mixture Distribution
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14
PyTorch implementation of the mixture distribution family with implicit reparametrisation gradients.
Mimo
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13
A toolbox for inference of mixture models
Jstacs
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12
Mixtcomp
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12
Model-based clustering package for mixed data
Mi2notes
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12
My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin
R Models
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9
A quick reference for how to run many models in R.
Vineclust
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8
Model-based clustering with vine copulas
Info337
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7
Herramientas estadísticas para la investigación
Mixmvpln
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7
R Package to Perform Clustering of Three-way Count Data Using Mixtures of Matrix Variate Poisson-log Normal Model With Parameter Estimation via MCMC-EM, Variational Gaussian Approximations, or a Hybrid Approach Combining Both.
Mixture_of_experts_keras
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7
Mixture of experts on convolutional neural network using Keras and Cifar10
Zoid
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7
Trinomial mixture models in Stan, for fitting to compositional data with 0s
Tsmc_dl
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6
TSMC course materials for unsupervised learning
Messi
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6
A predictive framework to identify signaling genes active in cell-cell interaction
Hmmufotu
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6
An HMM and Phylogenetic Placement based Ultra-Fast Taxonomy Assignment Tool for 16S sequencing
Aldvmm
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5
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 multi
Mts_outlierdetection
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5
Github page:
Grainlearning
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5
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).
1-34 of 34 search results
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