BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20250704T213101EDT-6366kMXRaG@132.216.98.100
DTSTAMP:20250705T013101Z
DESCRIPTION:Title: A new computational approach to fit hierarchical models 
 with INLA.\n\nAbstract:Sara is Associate Professor at the Department of Ma
 thematical Science at the Norwegian University of Science and Technology\,
  Norway. Her research focuses on spatial and computational Bayesian statis
 tics with a particular focus on approximate Bayesian inference methods. Sh
 e is interested in applications in a wide range of scientific fields from 
 ecology and air quality to multistate survival models.\n\n\nThe integrated
  nested Laplace approximation (INLA) is a deterministic approach to Bayesi
 an inference on latent Gaussian models (LGMs) and focuses on fast and accu
 rate approximation of posterior marginals for the parameters in the models
 . Recently\, methods have been developed to extend this class of models to
  those that can be expressed as conditional LGMs by fixing some of the par
 ameters in the models to descriptive values.\n	\n	These methods differ in th
 e manner descriptive values are chosen. In this talk we propose to combine
  importance sampling with INLA (IS-INLA)\, and extends this approach with 
 the more robust adaptive multiple importance sampling algorithm combined w
 ith INLA (AMIS-INLA). We compare these approaches and existing methods on 
 a series of applications with simulated and observed datasets and evaluate
 s their performance based on accuracy\, efficiency\, and robustness. The a
 pplications show that the AMIS-INLA approach\, in general\, outperforms th
 e other methods compared\, but the IS-INLA algorithm could be considered f
 or faster inference when good proposals are available.\n\nPlease visit our
  website for the Zoom Link: /epi-biostat-occh/seminar
 s-events/seminars/biostati...\n\nWeb site : /epi-bios
 tat-occh/seminars-events/seminars/biostatistics\n\n \n
DTSTART:20221005T193000Z
DTEND:20221005T203000Z
SUMMARY:Sara Martino\, PhD\, Norwegian University of Science and Technology
 \, Norway
URL:/mathstat/channels/event/sara-martino-phd-norwegia
 n-university-science-and-technology-norway-342428
END:VEVENT
END:VCALENDAR