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      <div class="TdmEntry">Overall Objectives<ul><li><a href="./uid3.html">Presentation</a></li></ul></div>
      <div class="TdmEntry">Research Program<ul><li><a href="uid8.html&#10;&#9;&#9;  ">Introduction</a></li><li><a href="uid9.html&#10;&#9;&#9;  ">Main research topics</a></li></ul></div>
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      <div class="TdmEntry">New Results<ul><li><a href="uid49.html&#10;&#9;&#9;  ">Computable approximations for continuous-time Markov decision processes on Borel spaces based on empirical measures</a></li><li><a href="uid50.html&#10;&#9;&#9;  ">Decentralized Proportional Load Balancing</a></li><li><a href="uid51.html&#10;&#9;&#9;  ">Constrained and Unconstrained Optimal Discounted Control of Piecewise Deterministic Markov Processes</a></li><li><a href="uid52.html&#10;&#9;&#9;  ">Approximate Kalman-Bucy filter for continuous-time semi-Markov jump linear systems</a></li><li><a href="uid53.html&#10;&#9;&#9;  ">Investigation of asymmetry in E. coli growth rate</a></li><li><a href="uid54.html&#10;&#9;&#9;  ">Impulsive Control for Continuous-Time Markov Decision Processes: A Linear Programming Approach</a></li><li><a href="uid55.html&#10;&#9;&#9;  ">Conditions for the Solvability of the Linear Programming Formulation for Constrained Discounted Markov Decision Processes</a></li><li><a href="uid56.html&#10;&#9;&#9;  ">Spatio-temporal averaging for a class of hybrid systems and application to conductance-based neuron models</a></li><li><a href="uid57.html&#10;&#9;&#9;  ">A comparison of fitness-case sampling methods for genetic programming</a></li><li><a href="uid58.html&#10;&#9;&#9;  ">Prediction of Expected Performance for a Genetic Programming Classifier</a></li><li><a href="uid59.html&#10;&#9;&#9;  ">Evolving Genetic Programming Classifiers with Novelty Search</a></li><li><a href="uid60.html&#10;&#9;&#9;  ">Regularity and Matching Pursuit Feature Extraction for the Detection of Epileptic Seizures</a></li><li><a href="uid61.html&#10;&#9;&#9;  ">Probabilistic safety analysis of the collision between a space debris and a satellite with an island particle algorithm</a></li><li><a href="uid62.html&#10;&#9;&#9;  ">Particle association measures and multiple target tracking </a></li><li><a href="uid63.html&#10;&#9;&#9;  ">Exponential mixing properties for time inhomogeneous diffusion processes with killing </a></li><li><a href="uid64.html&#10;&#9;&#9;  ">On particle Gibbs Markov chain Monte Carlo models</a></li><li><a href="uid65.html&#10;&#9;&#9;  ">Sequential Monte Carlo with Highly Informative Observations </a></li><li><a href="uid66.html&#10;&#9;&#9;  ">A duality formula for Feynman-Kac path particle models</a></li><li><a href="uid67.html&#10;&#9;&#9;  ">Non-Asymptotic Analysis of Adaptive and Annealed Feynman-Kac Particle Models</a></li><li><a href="uid68.html&#10;&#9;&#9;  ">Uniform stability of a particle approximation of the optimal filter derivative</a></li><li><a href="uid69.html&#10;&#9;&#9;  ">Combining clustering of variables and feature selection using random forests: the CoV/VSURF procedure</a></li><li><a href="uid70.html&#10;&#9;&#9;  ">An innovative approach combining animal performances, nutritional value and sensory quality of meat</a></li><li><a href="uid71.html&#10;&#9;&#9;  ">BIG-SIR: a Sliced Inverse Regression approach for massive data</a></li></ul></div>
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	    Raweb 
	    2016</a> | <a href="http://www.inria.fr/en/teams/cqfd">Presentation of the Project-Team CQFD</a></small>
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        <div class="Titrepage1">2016 Project-Team Activity Report
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          <div class="ProjetCourtpage1">CQFD</div>
          <div class="ProjetLongpage1">Quality control and dynamic reliability<div class="DescriptionTeam"/></div>
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          <span class="definition">Research centre: </span>
          <a href="http://www.inria.fr/centre/bordeaux">Bordeaux - Sud-Ouest</a>
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        <div class="partner"><span class="definition">In partnership with: </span>CNRS, Université de Bordeaux<br/><span class="definition">In collaboration with: </span>Institut de Mathématiques de Bordeaux (IMB)<br/><br/></div>
        <div class="domainepage1"><span class="definition">Field: </span><a href="&#10;&#9;      http://www.inria.fr/en/domains/Applied-Mathematics-Computation-and-Simulation">Applied Mathematics, Computation and Simulation</a><br/><span class="definition">Theme: </span>Stochastic approaches</div>
        <div class="Keywordspage">
          <span class="definition">Keywords: </span>
        </div>
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          <span class="definition2">
            <a href="/keywords/2016/computing">Computer Science and Digital Science: </a>
          </span>
          <ul>
            <li>1.1.6. - Cloud</li>
            <li>1.2.4. - QoS, performance evaluation</li>
            <li>1.3. - Distributed Systems</li>
            <li>3.3. - Data and knowledge analysis</li>
            <li>3.4.1. - Supervised learning</li>
            <li>3.4.2. - Unsupervised learning</li>
            <li>3.4.5. - Bayesian methods</li>
            <li>3.4.6. - Neural networks</li>
            <li>3.4.7. - Kernel methods</li>
            <li>5.9.2. - Estimation, modeling</li>
            <li>5.9.6. - Optimization tools</li>
            <li>6.1.2. - Stochastic Modeling (SPDE, SDE)</li>
            <li>6.1.3. - Discrete Modeling (multi-agent, people centered)</li>
            <li>6.2.2. - Numerical probability</li>
            <li>6.2.3. - Probabilistic methods</li>
            <li>6.2.4. - Statistical methods</li>
            <li>6.2.6. - Optimization</li>
            <li>6.4.2. - Stochastic control</li>
            <li>7.14. - Game Theory</li>
            <li>8.2. - Machine learning</li>
            <li>8.6. - Decision support</li>
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            <a href="/keywords/2016/other">Other Research Topics and Application Domains: </a>
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          <ul>
            <li>2.2.4. - Infectious diseases, Virology</li>
            <li>2.6.1. - Brain imaging</li>
            <li>5.9. - Industrial maintenance</li>
            <li>6.2. - Network technologies</li>
            <li>6.3.3. - Network Management</li>
            <li>6.5. - Information systems</li>
            <li>9.2.3. - Video games</li>
            <li>9.4.2. - Mathematics</li>
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