Fast Generation of von Mises-Fisher Distributed Pseudo-Random Vectors
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Updated
Mar 28, 2024 - R
Fast Generation of von Mises-Fisher Distributed Pseudo-Random Vectors
Clustering routines for the unit sphere
The following includes all the MATLAB scripts necessary for implementing the algorithm described in the attached paper.
This is the repository for the research project about the Generalized Procrustes Analysis using spatial anatomical information in fMRI data, i.e., the ProMises (Procrustes von Mises-Fisher) model
Fit and manipulate a few probability distribution functions on the unit S2 sphere.
Sampling from the von Mises - Fisher distribution
Spherical statistics in Python
Kernel density estimation on a sphere
Pytorch implementation of Hyperspherical Variational Auto-Encoders
Directional Co-clustering with a Conscience (DCC)
Code for EMNLP18 paper "Spherical Latent Spaces for Stable Variational Autoencoders"
Tensorflow implementation of Hyperspherical Variational Auto-Encoders
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