Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "12518"
#_source: array:26 [
"id" => "12518"
"slug" => "12518-bayesian-model-comparison-with-the-hyvarinen-score-computation-and-consistency"
"yearMonth" => "2019-09"
"year" => "2019"
"title" => "Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency"
"description" => "SHAO, S., JACOB, P., DING, J. et TAROKH, V. (2019). Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency. <i>Journal of the American Statistical Association</i>, 114(528), pp. 1826-1837. 
SHAO, S., JACOB, P., DING, J. et TAROKH, V. (2019). Bayesian Model Comparison with the Hyvärinen Sco "
"authors" => array:4 [
0 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
1 => array:1 [
"name" => "SHAO Stephane"
]
2 => array:1 [
"name" => "DING Jie"
]
3 => array:1 [
"name" => "TAROKH Vahid"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://doi.org/10.1080/01621459.2018.1518237"
"publicationInfo" => array:3 [
"pages" => "1826-1837"
"volume" => "114"
"number" => "528"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of out-of-sample predictive scores under the logarithmic scoring rule. However, when some of the candidate models involve vague priors on their parameters, the log-Bayes factor features an arbitrary additive constant that hinders its interpretation. As an alternative, we consider model comparison using the Hyvärinen score. We propose a method to consistently estimate this score for parametric models, using sequential Monte Carlo methods. We show that this score can be estimated for models with tractable likelihoods as well as nonlinear non-Gaussian state-space models with intractable likelihoods. We prove the asymptotic consistency of this new model selection criterion under strong regularity assumptions in the case of nonnested models, and we provide qualitative insights for the nested case The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of "
"en" => "The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of out-of-sample predictive scores under the logarithmic scoring rule. However, when some of the candidate models involve vague priors on their parameters, the log-Bayes factor features an arbitrary additive constant that hinders its interpretation. As an alternative, we consider model comparison using the Hyvärinen score. We propose a method to consistently estimate this score for parametric models, using sequential Monte Carlo methods. We show that this score can be estimated for models with tractable likelihoods as well as nonlinear non-Gaussian state-space models with intractable likelihoods. We prove the asymptotic consistency of this new model selection criterion under strong regularity assumptions in the case of nonnested models, and we provide qualitative insights for the nested case The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-16T09:21:41.000Z"
"docTitle" => "Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/jacob-pierre">JACOB Pierre</a>, SHAO Stephane, DING Jie, TAROKH Vahid"
"docDescription" => "<span class="document-property-authors">JACOB Pierre, SHAO Stephane, DING Jie, TAROKH Vahid</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2019</span> <span class="document-property-authors">JACOB Pierre, SHAO Stephane, DING Jie, TAROKH Vahid</span><b "
"keywordList" => ""
"docPreview" => "<b>Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency</b><br><span>2019-09 | Journal articles </span> <b>Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency</b><br><span>2019 "
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1080/01621459.2018.1518237" target="_blank">Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency</a> <a href="https://doi.org/10.1080/01621459.2018.1518237" target="_blank">Bayesian Model Comparison wi "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.678291
+"parent": null
}
