Essec\Faculty\Model\Contribution {#6196`
#_index: "academ_contributions"
#_id: "13386"
#_source: array:26 [``
"id" => "13386"
"slug" => "on-unbiased-estimation-for-discretized-models"
"yearMonth" => "2023-06"
"year" => "2023"
"title" => "On Unbiased Estimation for Discretized Models"
"description" => "HENG, J., JASRA, A., LAW, K. et TARAKANOV, A. (2023). On Unbiased Estimation for Discretized Models. <i>SIAM/ASA Journal on Uncertainty Quantification</i>, 11(2), pp. 10.1137/21M1460788."
"authors" => array:3 [``
0 => array:3 [``
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
`]
1 => array:1 [`
"name" => "LAW Kody"
`]
2 => array:1 [`
"name" => "TARAKANOV Alexander"
`]
]
"ouvrage" => ""
"keywords" => array:4 [`
0 => "Randomization methods"
1 => "Markov chain"
2 => "Monte Carlo"
3 => "Bayesian inverse problems"
`]
"updatedAt" => "2023-07-10 17:39:20"
"publicationUrl" => "https://doi.org/10.1137/21M1460788"
"publicationInfo" => array:3 [`
"pages" => "10.1137/21M1460788"
"volume" => "11"
"number" => "2"
`]
"type" => array:2 [`
"fr" => "Articles"
"en" => "Journal articles"
`]
"support_type" => array:2 [`
"fr" => "Revue scientifique"
"en" => "Scientific journal"
`]
"countries" => array:2 [`
"fr" => "États-Unis"
"en" => "United States of America"
`]
"abstract" => array:2 [`
"fr" => "In this article, we consider computing expectations w.r.t. probability measures which are subject to discretization error. Examples include partially observed diffusion processes or inverse problems, where one may have to discretize time and/or space in order to practically work with the probability of interest. Given access only to these discretizations, we consider the construction of unbiased Monte Carlo estimators of expectations w.r.t. such target probability distributions. It is shown how to obtain such estimators using a novel adaptation of randomization schemes and Markov simulation methods. Under appropriate assumptions, these estimators possess finite variance and finite expected cost. There are two important consequences of this approach: (i) unbiased inference is achieved at the canonical complexity rate, and (ii) the resulting estimators can be generated independently, thereby allowing strong scaling to arbitrarily many parallel processors. Several algorithms are presented and applied to some examples of Bayesian inference problems with both simulated and real observed data."
"en" => "In this article, we consider computing expectations w.r.t. probability measures which are subject to discretization error. Examples include partially observed diffusion processes or inverse problems, where one may have to discretize time and/or space in order to practically work with the probability of interest. Given access only to these discretizations, we consider the construction of unbiased Monte Carlo estimators of expectations w.r.t. such target probability distributions. It is shown how to obtain such estimators using a novel adaptation of randomization schemes and Markov simulation methods. Under appropriate assumptions, these estimators possess finite variance and finite expected cost. There are two important consequences of this approach: (i) unbiased inference is achieved at the canonical complexity rate, and (ii) the resulting estimators can be generated independently, thereby allowing strong scaling to arbitrarily many parallel processors. Several algorithms are presented and applied to some examples of Bayesian inference problems with both simulated and real observed data."
`]
"authors_fields" => array:2 [`
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
`]
"indexedAt" => "2024-02-27T07:21:46.000Z"
"docTitle" => "On Unbiased Estimation for Discretized Models"
"docSurtitle" => "Articles"
"authorNames" => "<a href="/cv/heng-jeremy">HENG Jeremy</a>, LAW Kody, TARAKANOV Alexander"
"docDescription" => "<span class="document-property-authors">HENG Jeremy, LAW Kody, TARAKANOV Alexander</span><br><span class="document-property-authors_fields">Systèmes d’Information, Sciences de la Décision et Statistiques</span> | <span class="document-property-year">2023</span>"
"keywordList" => "<a href="#">Randomization methods</a>, <a href="#">Markov chain</a>, <a href="#">Monte Carlo</a>, <a href="#">Bayesian inverse problems</a>"
"docPreview" => "<b>On Unbiased Estimation for Discretized Models</b><br><span>2023-06 | Articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1137/21M1460788" target="_blank">On Unbiased Estimation for Discretized Models</a>"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.789477
+"parent": null
}