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Py-ssm
Jul 20, 2023
Bayesian learning and inference for state space models
This package has fast and flexible code for simulating, learning, and performing inference in a variety of state space models. Currently, it supports
- Hidden Markov Models HMM
- Auto-regressive HMMs ARHMM
- Input-output HMMs IOHMM
- Hidden Semi-Markov Models HSMM
- Linear Dynamical Systems LDS
- Switching Linear Dynamical Systems SLDS
- Recurrent SLDS rSLDS
- Hierarchical extensions of the above
- Partial observations and missing data
It supports the following observation models
- Gaussian
- Student’s
- Bernoulli
- Poisson
- Categorical
- Von Mises
Checkout these related ports:
- Zn_poly - C library for polynomial arithmetic
- Zimpl - Language to translate the LP models into .lp or .mps
- Zegrapher - Software for plotting mathematical objects
- Zarray - Dynamically typed N-D expression system based on xtensor
- Z3 - Z3 Theorem Prover
- Yices - SMT solver
- Yacas - Yet Another Computer Algebra System
- Xtensor - Multi-dimensional arrays with broadcasting and lazy computing
- Xtensor-python - Python bindings for xtensor
- Xtensor-io - Xtensor plugin to read/write images, audio files, numpy npz and HDF5
- Xtensor-blas - BLAS extension to xtensor
- Xspread - Spreadsheet program for X and terminals
- Xppaut - Graphical tool for solving differential equations, etc
- Xplot - X11 plotting package
- Xlife++ - XLiFE++ eXtended Library of Finite Elements in C++