WHAT IS FILTREX ?

FILTREX is a User-friendly Software for Parametric Identification, Model Comparison and Optimal Sequential Sampling of Experiments of Complex Microbiological Dynamic Systems by Nonlinear Filtering. It is written in MATLAB^©.

This package is developed in the MaIAGE Lab. of INRA - Jouy-en-Josas, 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.
• It is based on published theoretical results (convergence, ...).
• In further releases, several species will be simultaneously considered.

HOW TO CITE FILTREX ?

FILTREX Software, INRA, UR1404, MaIAGE, Jouy-en-Josas, France.

CONTRIBUTORS

Project Coordinator :

Jean-Pierre GAUCHI.

Scientific Advisors :

J-P. VILA, J-P. GAUCHI, P. Del MORAL.

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)

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)

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. Rapport technique, Unité MaIAGE de Jouy-en-Josas, 2009-3, 45 pages PDF
• 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, 25-29 May 2009. DOI
• 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", September 2009, Washington, USA.
• Gauchi, J.-P., Bidot, C., Bouvier, A., Vila, J.-P., Coroller, L., Augustin, J.-C., (2013). A new user-friendly software for parametric identification, model comparison and optimal sequential sampling of experiments of complex microbiological dynamic systems by nonlinear filtering. International Conference on Predictive Modelling in Food. Poster and demo. 16-20 September 2013, Paris.
• 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", September 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", September 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 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.

Licence
This package is distributed under the terms of the GNU General Public License (GPL version 3 or later), in hope it will be useful but WITHOUT ANY WARRANTY.
A copy of the GNU General Public License GPL-3 is available at www.gnu.org/licenses/gpl-3.0.txt.

Requirements
MATLAB^© system. FILTREX has been tested on version R2012a and R2013a. It does not work on MATLAB R2014b. Displays are optimized for Linux platforms. On Windows platforms, some display problems may occur, but without consequence on calculations. Some tasks need a C compilator.

Download
Latest release is 3.0 (R2015a)

Contact
Jean-Pierre.Gauchi AT inra.fr
Annie.Bouvier AT inra.fr