26 citations · 88 across the 19 of their papers we have counts for
20 papers
Estimating parameters of continuous-time multi-chain hidden Markov models for infectious diseases
Ibrahim Bouzalmat, Benoîte de Saporta, Solym M. Manou-Abi
This study aims to estimate the parameters of a stochastic exposed-infected epidemiological model for the transmission dynamics of notifiable infectious diseases, based on observat…
Medical follow-up optimization: A Monte-Carlo planning strategy
Benoîte de Saporta, Aymar Thierry d'Argenlieu, Régis Sabbadin +1
Designing patient-specific follow-up strategy is a crucial step towards personalized medicine in cancer. Tools to help doctors deciding on treatment allocation together with next v…
Parameter estimation for a hidden linear birth and death process with immigration
Ibrahim Bouzalmat, Benoîte de Saporta, Solym M. Manou-Abi
In this paper, we use a linear birth and death process with immigration to model infectious disease propagation when contamination stems from both person-to-person contact and cont…
Numerical method to solve impulse control problems for partially observed piecewise deterministic Markov processes
Alice Cleynen, Benoîte de Saporta
Designing efficient and rigorous numerical methods for sequential decision-making under uncertainty is a difficult problem that arises in many applications frameworks. In this pape…
Stochastic Control of Observer Trajectories in Bearings-only Tracking with Acoustic Signal Propagation Optimization
Huilong Zhang, Benoîte de Saporta, François Dufour +2
We present in this paper a numerical method which computes the optimal trajectory of a underwater vehicle subject to some mission objectives. The method is applied to a submarine w…
Optimization of a launcher integration process: a Markov decision process approach
Christophe Nivot, Benoîte de Saporta, François Dufour +2
This paper is dedicated to the numerical study of the optimization of an industrial launcher integration process. It is an original case of inventory-production system where a cale…
Linear minimum mean square filters for Markov jump linear systems
Eduardo F. Costa, Benoîte de Saporta
New linear minimum mean square estimators are introduced in this paper by considering a cluster information structure in the filter design. The set of filters constructed in this w…
Investigation of asymmetry in E. coli growth rate
Bernard Delyon, Benoîte de Saporta, Nathalie Krell +1
The data we analyze derives from the observation of numerous cells of the bacterium Escherichia coli (E. coli) growing and dividing. Single cells grow and divide to give birth to t…
Approximate Kalman-Bucy filter for continuous-time semi-Markov jump linear systems
Benoîte de Saporta, Eduardo F. Costa
The aim of this paper is to propose a new numerical approximation of the Kalman-Bucy filter for semi-Markov jump linear systems. This approximation is based on the selection of typ…
Predictive maintenance for the heated hold-up tank
Benoîte de Saporta, Huilong Zhang
We present a numerical method to compute an optimal maintenance date for the test case of the heated hold-up tank. The system consists of a tank containing a fluid whose level is c…
Optimal stopping for partially observed piecewise-deterministic Markov processes
Adrien Brandejsky, Benoîte de Saporta, François Dufour
This paper deals with the optimal stopping problem under partial observation for piecewise-deterministic Markov processes. We first obtain a recursive formulation of the optimal fi…
Statistical study of asymmetry in cell lineage data
Benoîte de Saporta, Anne Gégout Petit, Laurence Marsalle
A rigorous methodology is proposed to study cell division data consisting in several observed genealogical trees of possibly different shapes. The procedure takes into account miss…
Random coefficients bifurcating autoregressive processes
Benoîte de Saporta, Anne Gégout-Petit, Laurence Marsalle
This paper presents a model of asymmetric bifurcating autoregressive process with random coefficients. We couple this model with a Galton Watson tree to take into account possibly…
Asymmetry tests for Bifurcating Auto-Regressive Processes with missing data
Benoîte de Saporta, Anne Gégout-Petit, Laurence Marsalle
We present symmetry tests for bifurcating autoregressive processes (BAR) when some data are missing. BAR processes typically model cell division data. Each cell can be of one of tw…
Numerical method for expectations of piecewise-determistic Markov processes
Adrien Brandejsky, Benoîte de Saporta, François Dufour
We present a numerical method to compute expectations of functionals of a piecewise-deterministic Markov process. We discuss time dependent functionals as well as deterministic tim…
Optimal stopping for the predictive maintenance of a structure subject to corrosion
Benoîte de Saporta, François Dufour, Huilong Zhang +1
We present a numerical method to compute the optimal maintenance time for a complex dynamic system applied to an example of maintenance of a metallic structure subject to corrosion…
Numerical methods for the exit time of a piecewise-deterministic Markov process
Adrien Brandejsky, Benoîte de Saporta, François Dufour
We present a numerical method to compute the survival function and the moments of the exit time for a piecewise-deterministic Markov process (PDMP). Our approach is based on the qu…
Parameters estimation for asymmetric bifurcating autoregressive processes with missing data
Benoîte de Saporta, Anne Gégout-Petit, Laurence Marsalle
We estimate the unknown parameters of an asymmetric bifurcating autoregressive process (BAR) when some of the data are missing. In this aim, we model the observed data by a two-typ…
Numerical method for impulse control of Piecewise Deterministic Markov Processes
Benoîte de Saporta, François Dufour
This paper presents a numerical method to calculate the value function for a general discounted impulse control problem for piecewise deterministic Markov processes. Our approach i…
Numerical method for optimal stopping of piecewise deterministic Markov processes
Benoîte de Saporta, François Dufour, Karen Gonzalez
We propose a numerical method to approximate the value function for the optimal stopping problem of a piecewise deterministic Markov process (PDMP). Our approach is based on quanti…