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      <div class="TdmEntry">Research Program<ul><li><a href="uid11.html&#10;&#9;&#9;  ">Interacting Monte Carlo methods
and particle approximation of Feynman–Kac distributions</a></li><li><a href="uid14.html&#10;&#9;&#9;  ">Multilevel splitting for rare event simulation</a></li><li><a href="uid25.html&#10;&#9;&#9;  ">Statistical learning: pattern recognition
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	    Raweb 
	    2016</a> | <a href="http://www.inria.fr/en/teams/aspi">Presentation of the Project-Team ASPI</a> | <a href="http://www.irisa.fr/aspi/index-en.html">ASPI Web Site
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        <h2>Section: 
      Highlights of the Year</h2>
        <h3 class="titre3">Highlights of the Year</h3>
        <p>Frédéric Cérou and Arnaud Guyader have received the
<a href="http://imstat.org/aihp/prizes.html">prize</a>
of the best recent paper published in the journal <i>Annales de l'Institut
Henri Poincaré, Probabilités et Statistiques</i>
for their joint paper <a href="./bibliography.html#aspi-2016-bid31">[3]</a>
in collaboration with Gérard Biau (université Pierre et Marie Curie).
This paper analyzes ABC (approximate Bayesian computation) —
a family of computational techniques which offer an almost automated
solution in situations where evaluation of the likelihood is computationally
prohibitive, or whenever suitable likelihoods are not available —
from the point of view of <span class="math"><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>k</mi></math></span>—nearest neighbor theory
and it explores the statistical properties of its outputs.
The paper discusses in particular some asymptotic features of the genuine
conditional density estimate associated with ABC, which is an interesting
hybrid between a <span class="math"><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>k</mi></math></span>–nearest neighbor and a kernel method.</p>
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