mcdickenson / em-gaussianLinks
Python code for Expectation-Maximization estimate of Gaussian mixture model
☆75Updated 6 years ago
Alternatives and similar repositories for em-gaussian
Users that are interested in em-gaussian are comparing it to the libraries listed below
Sorting:
- pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.☆216Updated 6 years ago
- Bayesian Gaussian mixture models in Python.☆63Updated 2 years ago
- ☆39Updated 8 years ago
- Collection of probabilistic models and inference algorithms☆241Updated 5 years ago
- A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)☆349Updated 8 years ago
- This is the code for "Gaussian Mixture Models - The Math of Intelligence (Week 7)" By Siraj Raval on Youtube☆146Updated 6 years ago
- Probabilistic Principal Component Analysis☆63Updated 8 years ago
- Code for Implementation, Inference, and Learning of Bayesian and Markov Networks along with some practical examples.☆104Updated 12 years ago
- A python tutorial for a Bayesian treatment of Linear Regression: https://zjost.github.io/bayesian-linear-regression/☆82Updated 8 years ago
- ☆83Updated 8 years ago
- Additional kernels that can be used with scikit-learn's Gaussian Process module☆82Updated last year
- Deep Gaussian Processes in Python☆234Updated 4 years ago
- Probabilistic graphical models in python☆24Updated 6 years ago
- Implementation of stochastic variational inference for Bayesian hidden Markov models.☆68Updated 7 years ago
- In probability theory and information theory, the mutual information of two random variables is a quantity that measures the mutual depen…☆78Updated 13 years ago
- Running parametric t-SNE by Laurens Van Der Maaten with Octave and oct2py.☆266Updated 6 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆249Updated 11 months ago
- ☆52Updated 7 years ago
- Deep Gaussian Processes in matlab☆93Updated 3 years ago
- Experiments with beta-VAE to learn disentangled representations from the data☆65Updated 6 years ago
- This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.☆34Updated 9 years ago
- Variational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"☆39Updated 8 years ago
- Code and data for the second edition of the textbook `Machine Learning: An Algorithm Perspective" by Stephen Marsland.☆149Updated 4 years ago
- Master Thesis on Bayesian Convolutional Neural Network using Variational Inference☆264Updated 6 years ago
- A tutorial about Gaussian process regression☆188Updated 4 years ago
- Some example scripts on pytorch☆198Updated 3 years ago
- State space modeling with recurrent neural networks☆45Updated 7 years ago
- Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another c…☆144Updated 2 years ago
- Variational Inference in Gaussian Mixture Model☆59Updated 4 years ago
- Bayesian nonparametric machine learning for Python☆224Updated last year