Skip to content
/ SMC Public

Likelihood calculation for stochastic models using particle filtering

Notifications You must be signed in to change notification settings

pints-team/SMC

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Likelihood calculation for stochastic models using particle filtering

A simple implementation of a particle filter for both linear Gaussian and logistic regression models. More details can be found in the attached report (soon).

Requirements:

  • numpy
  • pandas
  • seaborn
  • scipy
  • math
  • matplotlib

Files:

  • PFClasses.py -- the source code of the particle filter and model classes
  • PFExamples.ipynb -- some examples of using the particle filter
  • PFPints.ipynb -- some quick examples of integrating the particle filter as a pints.LogLikelihoodProblem (from the pints library. also requires the pints library installed)

About

Likelihood calculation for stochastic models using particle filtering

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published