Scalable and user friendly neural :brain: forecasting algorithms.

R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks

Model-based clustering package for mixed data

Implementation of GMMs optimized with the EM algorithm

Matlab code for the adaptive annealed importance sampling based marginal likelihood estimator.

Model-based subclonal deconvolution from bulk sequencing.

Multimodal Exponentially Modified Gaussians with Optional Oscillation

c++ library for parallel and distributed estimation of mixture model components using variational inference.

My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin

This program provides a look into the application of neural networks to predict the mass excess for nuclei which have yet to be experimentally measured.

Github page:

This R tutorial automates the 3-step ML auxiliary variable procedure using the MplusAutomation package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters. To learn more about auxiliary variable integration methods and why multi-step methods are necessary for producing un-biased estimates see Asparouhov & Muthén (2014).

Demonstrate the speed of running an LCA analysis using MplusAutomation

Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)

Supervised, unsupervised and semi-supervised classification with mixture modelling

A predictive framework to identify signaling genes active in cell-cell interaction

This R package allows the estimation and prediction for a clustered Gaussian process model proposed by Sung, Haaland, Hwang, and Lu (2023) in Statistica Sinica (https://www3.stat.sinica.edu.tw/ss_newpaper/SS-2020-0456_na.pdf)

Gaussian Parsimonious Clustering Models with Gating and Expert Network Covariates

Herramientas estadísticas para la investigación

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.

Estimate Gaussian mixture models using the Continuous Empirical Characteristic Function method introduced in (Xu & Knight, 2010)

Mixture of Linear Models Co-supervised by Deep Neural Network

Inflated Discrete Choice Models

Machine learning projects for financial market prediction; Python

MNIST Classification using GMM and VGG

Jupyter notebooks and other stuff with random research

VIBER - Variational Binomial Mixtures