mixture-of-gaussians

PyTorch implementation of the mixture distribution family with implicit reparametrisation gradients.

A sample application to detect motions based on Mixture of Gaussian algorithm

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

Fit mixtures of normal distributions to asset return data

Source code of "Semi-Supervised Clustering with Inaccurate Pairwise Annotations" (Gribel, Gendreau and Vidal, 2021)

Fit mixtures of normals and other base distributions to asset returns

MDMAD is a diffusion-based generative model for antibody design that uses mixture distributions to model multimodal sequence–structure transitions. It is designed to better capture diverse CDR conformations while maintaining competitive structural and sequence quality

A Wasserstein Generative Adversarial Network that learns the distribution of a Mixture of Gaussian, using weight clipping or spectral normalization

Homeworks of CMPE462 course in Bogazici University

Advanced Background Subtraction using OpenCV

End to End Basic object Detection using Open CV

Fit a univariate mixture of normals to simulated data using the EM algorithm, in Python

This is an implementation of the 2D Mixture of Gaussians (MOG) model based on Toscano & McMurray (2010) which was used in my Master's Thesis (Differential Cue Weighting in Sibilants: A Case Study of Two Sinitic Languages).

Advanced Background Subtraction using OpenCV

These are the essential machine learning algorithms that I implemented for Introduction to Machine Learning lecture in my university.

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

Estimate a univariate mixture of normals distribution using moments and the EM algorithm