{"id":5076,"date":"2023-11-16T11:29:13","date_gmt":"2023-11-16T10:29:13","guid":{"rendered":"https:\/\/www.bordeaux-population-health.center\/?p=5076"},"modified":"2023-11-27T10:38:27","modified_gmt":"2023-11-27T09:38:27","slug":"biostatistics-seminar-optimal-control-for-parameter-estimation","status":"publish","type":"post","link":"https:\/\/www.bordeaux-population-health.center\/fr\/biostatistics-seminar-optimal-control-for-parameter-estimation\/","title":{"rendered":"BIOSTATISTICS SEMINAR : Optimal control for parameter estimation in partially observed hypoelliptic stochastic differential equations used in neuronal modeling
– Quentin CLAIRON"},"content":{"rendered":"
\n

\"\"<\/p>\n

SEMINAIRE BIOSTATISTICS<\/strong>
\nOptimal control for parameter estimation
\nin partially observed hypoelliptic stochastic differential equations
\nused in neuronal modeling<\/strong><\/p>\n

13h
\nSalle Nightingale – Isped
\nCampus Carreire – Universit\u00e9 de Bordeaux<\/div>\n
\n
Un travail commun avec Adeline LECLERCQ-SAMSON<\/strong><\/div>\n

Ouvert \u00e0 tout le monde, en pr\u00e9sentiel, en anglais<\/p>\n

Intervenant :<\/div>\n

Quentin CLAIRON<\/span>
\nUniversit\u00e9 de Bordeaux, Inria Bordeaux Sud-Ouest, Inserm, Bordeaux Population Health Research Center, SISTM Team.<\/p>\n

+ d’information sur Quentin CLAIRON<\/strong><\/a><\/p>\n

Abstract:<\/div>\n

The complex behavior of a neuron activity (brutal membrane potential oscillations, random spiking time) requires stochastic differential equations (SDE)s for proper modeling of its temporal evolution.
\nStill, in such neuronal models not all variable evolutions are directly affected by stochastic perturbations.
\nThis leads to consider hypoelliptic SDEs characterized by degenerate diffusion coefficients.
\nAdditionally, the statistical problem of parameter estimation often must be treated in a partially observed setting where we only have access to measurements for the variables the less affected by stochasticity. These features often cause the failure of classic estimation procedures based on contrast estimator and the Euler Maruyama discretization scheme as well as dramatically impairs classic stochastic filtering methods used to reconstruct the unobserved states.
\nAll these issues make the estimation problem in hypoelliptic SDEs difficult to solve.
\nTo overcome this, we propose a method based a well-defined cost function no matter the elliptic or hypoelliptic nature of the model. We also bypass the filtering step by considering a control theory perspective.
\nThe unobserved states are estimated by solving deterministic optimal control problems using numerical methods which do not need strong assumptions on the diffusion coefficient conditioning.
\nNumerical simulations made on different partially observed hypoelliptic SDEs reveal our method produces accurate estimates while dramatically reducing the computational price compared to other methods.<\/p>\n

Pr\u00e9c\u00e9dents e-s\u00e9minaires Biostatistics sur la cha\u00eene YouTube
\nhttps:\/\/www.youtube.com\/channel\/UCURp-hEQL7k23UzGfqgEurA\/videos<\/a><\/strong><\/div>\n

 <\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

SEMINAIRE BIOSTATISTICS Optimal control for parameter estimation in partially observed hypoelliptic stochastic differential equations used in neuronal modeling 13h Salle Nightingale – Isped Campus Carreire – Universit\u00e9 de Bordeaux Un travail commun avec Adeline LECLERCQ-SAMSON Ouvert \u00e0 tout le monde, en pr\u00e9sentiel, en anglais Intervenant : Quentin CLAIRON Universit\u00e9 de Bordeaux, Inria Bordeaux Sud-Ouest, Inserm, […]<\/p>\n","protected":false},"author":613,"featured_media":5067,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[26],"tags":[],"class_list":["post-5076","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-seminaires"],"acf":[],"yoast_head":"\nBIOSTATISTICS SEMINAR : Optimal control for parameter estimation in partially observed hypoelliptic stochastic differential equations used in neuronal modeling- Quentin 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