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      <div class="TdmEntry">Overall Objectives<ul><li><a href="./uid3.html">Developing sound, useful and usable methods</a></li><li><a href="./uid4.html">Combining numerical, statistical and stochastic components of a model</a></li><li><a href="./uid5.html">Developing future standards</a></li></ul></div>
      <div class="TdmEntry">Research Program<ul><li><a href="uid10.html&#10;&#9;&#9;  ">Scientific positioning</a></li><li><a href="uid11.html&#10;&#9;&#9;  ">The mixed-effects models</a></li><li><a href="uid14.html&#10;&#9;&#9;  ">Computational Statistical Methods</a></li><li><a href="uid18.html&#10;&#9;&#9;  ">Markov Chain Monte Carlo algorithms</a></li><li><a href="uid21.html&#10;&#9;&#9;  ">Parameter estimation</a></li><li><a href="uid27.html&#10;&#9;&#9;  ">Model building</a></li><li><a href="uid28.html&#10;&#9;&#9;  ">Model evaluation</a></li><li><a href="uid29.html&#10;&#9;&#9;  ">Missing data</a></li></ul></div>
      <div class="TdmEntry">Application Domains<ul><li><a href="uid34.html&#10;&#9;&#9;  ">Population pharmacometrics</a></li><li><a href="uid35.html&#10;&#9;&#9;  ">Precision medicine and pharmacogenomics</a></li><li><a href="uid36.html&#10;&#9;&#9;  ">Biology - Intracellular processes</a></li></ul></div>
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      <div class="TdmEntry">New Software and Platforms<ul><li><a href="uid40.html&#10;&#9;&#9;  ">mlxR</a></li><li><a href="uid41.html&#10;&#9;&#9;  ">FactoMineR</a></li><li><a href="uid42.html&#10;&#9;&#9;  ">missMDA</a></li><li><a href="uid43.html&#10;&#9;&#9;  ">denoiseR</a></li></ul></div>
      <div class="TdmEntry">New Results<ul><li><a href="uid45.html&#10;&#9;&#9;  ">Identifiability in mixed effects models</a></li><li><a href="uid46.html&#10;&#9;&#9;  ">Enhanced Method for Diagnosing Pharmacometric Models</a></li><li><a href="uid47.html&#10;&#9;&#9;  ">A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection</a></li><li><a href="uid48.html&#10;&#9;&#9;  ">Maximum likelihood estimation of a low-order building model</a></li><li><a href="uid49.html&#10;&#9;&#9;  ">LP-convergence of a Girsanov theorem based particle filter</a></li><li><a href="uid50.html&#10;&#9;&#9;  ">Adaptive estimation in the nonparametric random coefficients binary choice model by needlet thresholding</a></li></ul></div>
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      <div class="TdmEntry">Dissemination<ul><li><a href="uid61.html&#10;&#9;&#9;  ">Promoting Scientific Activities</a></li><li><a href="uid68.html&#10;&#9;&#9;  ">Teaching - Supervision - Juries</a></li></ul></div>
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	    Inria
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	    Raweb 
	    2016</a> | <a href="http://www.inria.fr/en/teams/xpop">Presentation of the Team XPOP</a></small>
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        <div class="Titrepage1">2016 Team Activity Report
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          <div class="ProjetCourtpage1">XPOP</div>
          <div class="ProjetLongpage1">statistical modelling for life sciences<div class="DescriptionTeam">Inria teams are typically groups of researchers working on the definition of a common project, and objectives, with the goal to arrive at the creation of a project-team. Such project-teams may include other partners (universities or research institutions).</div></div>
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          <span class="definition">Research centre: </span>
          <a href="http://www.inria.fr/centre/saclay">Saclay - Île-de-France</a>
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        <div class="domainepage1"><span class="definition">Field: </span><a href="&#10;&#9;      http://www.inria.fr/en/domains/Digital-Health-Biology-and-Earth">Digital Health, Biology and Earth</a><br/><span class="definition">Theme: </span>Computational Neuroscience and Medecine</div>
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          <span class="definition">Keywords: </span>
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            <a href="/keywords/2016/computing">Computer Science and Digital Science: </a>
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          <ul>
            <li>3.1.1. - Modeling, representation</li>
            <li>3.2.3. - Inference</li>
            <li>3.3. - Data and knowledge analysis</li>
            <li>3.3.1. - On-line analytical processing</li>
            <li>3.3.2. - Data mining</li>
            <li>3.3.3. - Big data analysis</li>
            <li>3.4.1. - Supervised learning</li>
            <li>3.4.2. - Unsupervised learning</li>
            <li>3.4.4. - Optimization and learning</li>
            <li>3.4.5. - Bayesian methods</li>
            <li>3.4.6. - Neural networks</li>
            <li>3.4.7. - Kernel methods</li>
            <li>3.4.8. - Deep learning</li>
            <li>5.9.2. - Estimation, modeling</li>
            <li>6.1.1. - Continuous Modeling (PDE, ODE)</li>
            <li>6.2.2. - Numerical probability</li>
            <li>6.2.3. - Probabilistic methods</li>
            <li>6.2.4. - Statistical methods</li>
            <li>6.3.3. - Data processing</li>
            <li>6.3.5. - Uncertainty Quantification</li>
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            <a href="/keywords/2016/other">Other Research Topics and Application Domains: </a>
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            <li>1.1.5. - Genetics</li>
            <li>1.1.6. - Genomics</li>
            <li>1.1.9. - Bioinformatics</li>
            <li>1.1.11. - Systems biology</li>
            <li>2.2.3. - Cancer</li>
            <li>2.2.4. - Infectious diseases, Virology</li>
            <li>2.4.1. - Pharmaco kinetics and dynamics</li>
            <li>9.1.1. - E-learning, MOOC</li>
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