AntoinePassemiers / ArchMM
A Cython Machine Learning library dedicated to Hidden Markov Models
☆35Updated last year
Alternatives and similar repositories for ArchMM:
Users that are interested in ArchMM are comparing it to the libraries listed below
- Markov Switching Models for Statsmodels☆22Updated 8 years ago
- Implementation of linear CorEx and temporal CorEx.☆37Updated 3 years ago
- Python software for selective inference☆52Updated last year
- Applications of Gaussian Process Latent Variable Models in Finance☆11Updated 2 years ago
- Python Copula Module☆42Updated 2 years ago
- c-lasso: a Python package for constrained sparse regression and classification☆32Updated 3 years ago
- Group Lasso package for Python.☆15Updated last year
- Hierarchical Change-Point Detection☆14Updated 6 years ago
- Maximum entropy and minimum divergence models in Python☆42Updated 10 months ago
- Implementation of Hidden Markov Models in pymc3☆60Updated 8 years ago
- Information Theoretic Tools for Python☆91Updated 5 years ago
- Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Su…☆77Updated last year
- Material for the practical of the DS3 course on "Representing and comparing probabilities with kernels"☆26Updated 6 years ago
- Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features☆23Updated 5 years ago
- Collapsed Variational Bayes☆70Updated 5 years ago
- Functional data analysis in python☆12Updated 9 years ago
- ☆74Updated 6 years ago
- L1 Trend Filtering☆19Updated 10 months ago
- A library of scalable Bayesian generalised linear models with fancy features☆60Updated 7 years ago
- Gaussian Process and Uncertainty Quantification Summer School 2018☆31Updated 2 years ago
- State space modeling with recurrent neural networks☆43Updated 6 years ago
- Bayesian model averaging of an objective function over a model class using advanced MCMC techniques.☆16Updated 10 years ago
- Fast, linear version of CorEx for covariance estimation, dimensionality reduction, and subspace clustering with very under-sampled, high-…