CQFD - 2014 - Annual activity report

CQFD

CQFD - 2014

Project-Team Cqfd

Members

Overall Objectives

Presentation

Research Program

Application Domains

Dependability and safety

New Software and Platforms

New Results

Highlights of the Year
Approximate Kalman–Bucy filter for continuous-time semi-Markov jump linear systems
Modeling and optimization of a launcher integration process
Numerical approximation for optimal stopping of MDP under partial observation
Classification of EEG signals by evolutionary algorithm
Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms
Variable selection to construct indicators of quality of life for data structured in groups
Efficiency of simulation in monotone hyper-stable queueing networks
Control of parallel non-observable queues: asymptotic equivalence and optimality of periodic policies
The economics of the cloud: price competition and congestion
Generalized Nash Equilibria for Platform-as-a-Service Clouds
Stochastic approximations of constrained discounted Markov decision processes
Non-Parametric Estimation of the Conditional Distribution of the Interjumping Times for Piecewise-Deterministic Markov Processes
Approximation of average cost Markov decision processes using empirical distributions and concentration inequalities
Piecewise Deterministic Markov Processes based approach applied to an offshore oil production system
Optimal Trajectories for Underwater Vehicles by Quantization and Stochastic control
Multi-Objective Design and Maintenance Optimization of the Heated Hold-Up Tank Modeled by Piecewise Deterministic Markov Processes
Conditional quantile estimation through optimal quantization
Conditional quantile estimator based on optimal quantization: from theory to practice
QuantifQuantile : an R package for performing quantile regression trough optimal quantization
Transcriptome profile analysis reveals specific signatures of pollutants in Atlantic eels
Comparaison of kernel density estimators with assumption on number of modes : application on environmental monitoring data
A new sliced inverse regression method for multivariate response
An introduction to dimension reduction in nonparametric kernel regression
Hidden Markov Model for the detection of a degraded operating mode of optronic equipment
On the asymptotic behavior of the Nadaraya-Watson estimator associated with the recursive SIR method
Evolving Genetic Programming Classifiers with Novelty Search
Detecting mental states of alertness with genetic algorithm variable selection
A comparison of fitness-case sampling methods for Symbolic Regression
Geometric Semantic Genetic Programming with Local Search

Bilateral Contracts and Grants with Industry

Partnerships and Cooperations

Dissemination

Bibliography

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Section: New Software and Platforms

Biips: Software for Bayesian Inference with Interacting Particle Systems

Biips is a software platform for automatic Bayesian inference with interacting particle systems. Biips allows users to define their statistical model in the probabilistic programming BUGS language, as well as to add custom functions or samplers within this language. Then it runs sequential Monte Carlo based algorithms (particle filters, particle independent Metropolis-Hastings, particle marginal Metropolis-Hastings) in a black-box manner so that to approximate the posterior distribution of interest as well as the marginal likelihood. The software is developed in C++ with interfaces with the softwares R, Matlab and Octave [53] .

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