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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)