Metadata-Version: 2.1
Name: statespace
Version: 1.3.39
Summary: state-space distributions and decisions
Home-page: https://gitlab.com/noahhsmith/statespace
Author: noah smith
Author-email: noahhsmith@gmail.com
License: UNKNOWN
Description: <img src="https://gitlab.com/noahhsmith/statespace/raw/master/docs/images/pf2-small.png"/>
        
        [![pipeline](https://gitlab.com/noahhsmith/starid/badges/master/pipeline.svg)](https://gitlab.com/noahhsmith/statespace/pipelines)
        [![pypi](https://img.shields.io/badge/pypi-latest-brightgreen.svg)](https://pypi.org/project/statespace/)
        [![docs](https://readthedocs.org/projects/statespace/badge/?version=latest)](https://statespace.readthedocs.io/en/latest/?badge=latest)
        
        reference problems from 
        
        [Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, James V. Candy](http://a.co/gp4upXd)
        
        [Kalman Filtering: Theory and Practice, Mohinder S. Grewal, Angus P. Andrews](http://a.co/6hAa35c)
        
        [Stochastic Processes and Filtering Theory, Jazwinski](https://amzn.to/2NLXfVK)
        
        210221
        
        brought the documentation via readthedocs up to a minimal level. cleaned up the project and brought some focus to what's going on here. as the docs now make clear - this project focuses on reference problems from Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, Kalman Filtering: Theory and Practice, and Stochastic Processes and Filtering Theory - in particular, using numpy for matrix and vector manipulation.
        
        190418
        
        brief [lit-review](https://www.linkedin.com/pulse/google-state-space-noah-smith/) posted on linkedin.
        
        190331
        
        concise [motivation piece](https://www.linkedin.com/pulse/shape-uncertainty-noah-smith/) posted on linkedin.
        
        190310
        
        decision-function-based detector is go. simplest possible case - linear rc-circuit system-model and linear kalman-filter tracker. log-likelihood decision function for detection, ensembles of 100 runs each for signal case and noise case. output curves shown in the first plot - green signal, blue noise-only. roc curves in the second plot. 
        
        ![](https://gitlab.com/noahhsmith/statespace/raw/master/docs/images/rccircdecfuncs.png)
         
        ![](https://gitlab.com/noahhsmith/statespace/raw/master/docs/images/rccircroc.png)
        
        <a name="190223">190223</a>
        
        kl-divergence for evaluating sequential monte-carlo - demonstrated below by three pf's in action during the first second of the jazwinksi problem - start-up and convergence. these are 100 hz dist-curves - each dist-curve is a kernel-density-estimate combining hundreds of monte-carlo samples, the fundamental-particles - green dist-curves for truth, blue dist-curves for pf. state-estimates are two red curves on the x,t-plane beneath the dist-curves.
        
        ![pf1](https://gitlab.com/noahhsmith/statespace/raw/master/docs/images/pf1.png)
        
        ![pf2](https://gitlab.com/noahhsmith/statespace/raw/master/docs/images/pf2.png)
        
        ![pf3](https://gitlab.com/noahhsmith/statespace/raw/master/docs/images/pf3.png)
        
        190105
        
        ukf adaptive jazwinksi switched to square-root filtering, qr-factorization, cholesky-factor update and downdate. improved numerical stability and scaled sampling is clear. still a question around scalar-obs and the obs cholesky-factor and gain. with an adhoc stabilizer on the obs cholesky-factor it's working well overall.
        
        181230
        
        pf adaptive jazwinksi. parameter-roughening.
        
        181226
        
        ukf adaptive jazwinski. sample-and-propagate tuning.
        
        180910
        
        ekf adaptive jazwinski. ud-factorized square-root filtering required for numerical stability.
        
            
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
