
FILTREX is a New User-friendly Software for Parametric Identification,
 Model Comparison and Optimal Sequential Sampling of Experiments of
 Complex Microbiological Dynamic Systems by Nonlinear Filtering.

FILTREX is developed in the Applied Mathematics and Informatics
Department of the French National Institute for Agricultural 
Research (INRA: Institut National de la Recherche Agronomique).

IDENTIFICATION
---------------
- SOFTWARE NAME : FILTREX
- VERSION :       3.0 (R2015a)
- LANGUAGE:       MATLAB and C
- LICENCE:        GPL >=3

CONTRIBUTORS
------------
- Project Coordinator :
 J-P. GAUCHI (INRA/MaIAGE-Jouy-en-Josas, France)

- Scientific Advisors : 
 J-P. VILA (INRA/MISTEA-Montpellier, France)
 J-P. GAUCHI (INRA/MaIAGE-Jouy-en-Josas, France)
 P. Del MORAL (INRIA/Bordeaux University, France)

- Main contributors to the source code (alphabetic order):
 C. BIDOT (INRA/MaIAGE-Jouy-en-Josas, France)
 A. BOUVIER (INRA/MaIAGE-Jouy-en-Josas, France)
 R. CHOQUET (CNRS/CEFE, Montpellier, France)
 V. ROSSI (PhD student 2002-2004, Montpellier University/INRA-ENSAM, France)

- Secondary contributors to the source code (alphabetic order):
 E. ATLIJANI (Technical trainee, 2009, INRA/MIA-Jouy-en-Josas, France)
 E. MAILLOT (Technical trainee, 2008, INRA/MISTEA-Montpellier, France)

IN WHICH CONTEXT YOU CAN USE FILTREX ?
------------------------------------
In the microbiological context of modelisation of complex
 microbiological dynamic systems characterised by:
- One or several bacteria species leading eventually to several 
simultaneous dynamic equations.
- The growth or the inactivation are not directly observable.
- Sophisticated dilution and counting stepwise processes associated 
to several experimental errors.

FILTREX OBJECTIVES
-----------------
- Parameter identification of the growth and inactivation models.
- Comparison and selection of these models.
- Optimal sequential sampling of experiments (three options).

FILTREX MATHEMATICAL FRAMEWORK
------------------------------
- A nonlinear particle filtering method based on a new nonlinear
 particular (nonparametrical) filtering technique using a convolution
 kernel approach and a particular resampling trick.
- Nonlinear autoregressive dynamic systems simultaneously defined by
 a stochastic state equation and an observation equation.
- Not directly observable systems.
- Non explicite likelihood function.

FILTREX ADVANTAGES
------------------
- No initial guesses for parameters are needed: postulated parameter
 intervals are only necessary (they can be broad in a first step if 
only very few information is available on parameters).
- The experimental errors (samplings, countings, ...) are better 
taken into account, and their coefficients of variation can be also
estimated.
- Several species can be simultaneously considered.
- It is based on published theoretical results (convergence, ...)

HOW TO CITE FILTREX ?
--------------------
FILTREX Software,  INRA, UR1404, Jouy-en-Josas, France.

REFERENCES
----------
- Bidot, C., Gauchi, J.-P., Vila, J.-P. (2009). Programmation MATLAB 
du filtrage non linéaire par convolution de particules pour 
l’identification et l’estimation d’un système dynamique 
microbiologique, Version 2009_1. Rapport technique de l’Unité MaIAGE
de Jouy-en-Josas, n°2009-3, 45 pages.

- Bidot, C., Gauchi, J.-P., Vila, J.-P. (2009). Identification de
systèmes dynamiques microbiologiques complexes par filtrage non 
linéaire. Actes des 41ièmes Journées de Statistique (SFdS), Bordeaux,
25 mai au 29 mai 2009.

- Choquet R. and Rossi V. (2005) Routines pour le filtrage 
particulaire.  Rapport CEFE-CNRS.

- Gauchi, J.-P., Bidot, C., Augustin, J.-C., Vila, J.-P. (2009).
Identification of complex microbiological dynamic systems by 
nonlinear filtering. 6th International Conference 
“Predictive Modelling in Foods”, Septembre 2009, Washington, USA.

- Gauchi, J.-P., Vila, J.-P., Bidot C., Atlijani E., Coroller L.,  
Augustin J.-C., and Del Moral P. (2011). FILTREX : A new software for
identification and optimal sampling of experiments for complex 
microbiological dynamic systems by nonlinear filtering. 
In Abstracts of 7th. International Conference 
"Predictive Modelling in Foods", Septembre 2011, Dublin, Ireland

- Gauchi, J.-P., Vila, J.-P. (2011). Optimal sequential sampling 
design for improving parametric identification of complex 
microbiological dynamic systems by nonlinear filtering. Poster at 7th 
International Conference “Predictive Modelling in Foods”, 
Septembre 2011, Dublin, Ireland.

- Gauchi, J.-P., Vila, J.-P., Bidot, C., Bouvier, A., Coroller, 
L., Augustin, J.-C., Del Moral, P., (2012). FILTREX: Un logiciel 
convivial pour la microbiologie alimentaire prévisionnelle. 
Modélisation dynamique de la croissance ou décroissance de populations
bactériennes. Poster aux Journées des microbiologistes de l’INRA 2012.

- Gauchi J.-P. and Vila J.-P. (2013) Nonparametric particle filtering
approaches for identification and inference in nonlinear state-space 
dynamic systems. Statistics and Computing, 23:523-533.

- Rossi V. and Vila J.P. (2005) Approche non paramétrique du filtrage
de système non linéaire à temps discret et à paramètres inconnus. 
C.R. Acad. Sci. Paris. Ser I 340, 759-764.

- Rossi V. and Vila J.P. (2006) Nonlinear filtering in discrete time:
a particle convolution approach. Inst. Stat. Univ. Paris, 3, 71-102.

- Vila J.P. and Saley I. (2009) Bayes Factor estimation for nonlinear
dynamic state space models. C.R. Acad. Sci., Paris, Ser. I 347, 429-434.
