BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:Europe/Paris X-WR-TIMEZONE:Europe/Paris BEGIN:VEVENT UID:7212@i2m.univ-amu.fr DTSTART;TZID=Europe/Paris:20181022T000000 DTEND;TZID=Europe/Paris:20181026T000000 DTSTAMP:20241120T203450Z URL:https://www.i2m.univ-amu.fr/evenements/masterclass-in-bayesian-statist ics-morlet-chair-kerrie-mengersen/ SUMMARY:School (CIRM\, Luminy\, Marseille): Masterclass in Bayesian Statist ics (Morlet Chair Kerrie Mengersen) DESCRIPTION:School: \n\n\n\n CIRM - Jean-Morlet Chair \n Kerrie MENGERSEN & amp\; Pierre PUDLO\n\nBayesian Modelling and Analysis of Big data\n\nModé lisation bayésienne et analyse du Big data\n\n\n 2018-Semester 2 \n\n\n\n \n\n\n\n\n\n\n\nRESEARCH SCHOOL - ECOLE DE RECHERCHE\nMasterclass in Bayes ian Statistics (1854)\nMasterclass en statistiques bayésiennes\nDates: 2 2-26 October 2018\nPlace: CIRM (Marseille Luminy\, France)\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n SCHEDULE \n\n\n\n\n\n ABSTRACTS \n\n\n\n\n\n PARTICIP ANTS \n\n\n\n\n\n\n \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ n\nRESUME\n\nLa plupart des domaines scientifiques sont aujourd'hui confro ntés à la question des "Big Data"\, c'est-à-dire l'afflux massif de don nées\, potentiellement à structures multiples et complexes. Pour faire f ace à ce déluge de données\, l'approche bayésienne semble particulièr ement prometteuse car elle permet\, par la spécification d'une distributi on préalable sur le système inconnu\, de structurer des problèmes de gr ande dimension\, soit en exploitant une expertise préalable sur le phéno mène observé\, soit en utilisant des outils génériques de modélisatio n tels que les processus gaussiens. Cette Masterclass vise à introduire d es outils algorithmiques et inférentiels nouveaux et de pointe.\n\n\n\n\n \n\n\n\n\n\n\n\nDESCRIPTION\n\nMost scientific fields now face the issue o f “big data”\, ie the influx of massive datasets\, potentially with mu ltiple and complex structure. To deal with this data deluge\, the Bayesian approach sounds particularly promising as it allows\, through the specifi cation of a prior distribution on the unknown system\, to add structure to problems of large dimension\, either by exploiting prior expertise on the observed phenomenon\, or by using generic modelling tools such as Gaussia n processes. As a concrete example\, consider brain imaging in tumor detec tion: the dimension of the problem is the number of voxels (i.e.\, unitary elements of an image in three dimensions\, which typically range in the o rder of a million objects)\, and a prior distribution makes it possible to impose that neighboring voxels are similar with high probability\, to ref lect the structure of gray matter. However\, Bayesian approaches are still relatively rarely used in very large problems because the basic algorithm s for computing Bayes estimators (especially Markov chain Monte Carlo (MCM C) methods) may prove too costly in computing time and memory size. It is therefore often necessary\, when implementing a Bayesian approach in a non -trivial problem\, to turn to more advanced methods\, either on the comput ationally speaking (like an implementation on a parallel architecture) or on the mathematically speaking (e.g.\, convergence of approximate methods\ , use of continuous-time process). More precisely\, this masterclass schoo l aims at introducing novel and state-of-the art algorithmic and inferenti al tools\, from advanced algorithms (Approximate Bayesian computation (ABC )\, synthetic likelihood\, indirect inference\, noisy and consensus Monte Carlo\, Langevin diffusion subsampling\, Hamiltonian Monte Carlo\, sequent ial and asynchronous methods) to inference techniques for large data sets (synthetic likelihood\, indirect and non-parametric inference\, pseudolike lihood\, variational approaches\, automatic selection of summaries).\n\n\n \n\n\n[su_spacer]\nSCIENTIFIC COMMITTEE\n\n\n Nicolas Chopin (ENSAE Pari sTech)\n Christian P. Robert (Université Paris-Dauphine)\n Adeline Sam son (Université Grenoble Alpes)\n Sylvia Richardson (University of Cambr idge)\n\n\n\nORGANIZING COMMITTEE\n\n\n Nicolas Chopin (ENSAE ParisTech)\ n Kerrie Mengersen (QUT Brisbane)\n Denys Pommeret (Aix-Marseille Univer sité)\n Pierre Pudlo (Aix-Marseille Université)\n Christian P. Robert  (Université Paris-Dauphine)\n Robin Ryder (Université Paris-Dauphine) \n\n\n\n\n\nCOURSES\n\n Nicolas Chopin (ENSAE ParisTech) -\nA (gentle) i ntroduction to particle filters (pdf)  - VIDEO -\n Kerrie Mengersen (Qu eensland University of Technology) -\nIntroduction to Bayesian Statistical Modelling and Analysis (pdf)  - VIDEO - \n Christian P. Robert (Univ ersité Paris-Dauphine) -\nMarkov Chain Monte Carlo Methods (pdf)  - VID EO - \n Håvard Rue (KAUST) -\nBayesian computation with INLA (pdf)  - VIDEO - \n Aki Vehtari (Aalto University) -\n\nModel assessment\, selec tion and averaging (pdf)  - VIDEO - \nPrior and posterior predictive ch ecking (pdf)\nDynamic Hamiltonian Monte Carlo in Stan (pdf)\nGeneric MCM C convergence diagnostics  (pdf)\n\n\n\n\n\nPRACTICAL TUTORIALS\n\n Gui llaume Kon Kam King (Università degli Studi di Torino)  -  Good practic e in R: code and package development (pdf)\n Julien Stoehr (Université Paris-Dauphine) -  A short tutorial on RMarkdown and knitr (pdf) (zip)\n Aki Vehtari (Aalto University) - The Stan software  (pdf)\n\n\n\n\n\n\n \n\n\n\n\n\n\nSPEAKERS \n\n Simon Barthelmé (Gipsa-Lab Grenoble) -\n\n Variational Approximations and How to Improve Them (pdf)\n\n Marie-Pierre Etienne (AgroParisTech INRA) -\n\nSequential Monte Carlo smoother for par tially observed\ndiffusion processes (pdf)\n\n Chris Holmes (Oxford Univ ersity) -\n\nBayesian learning at scale with approximate models (pdf)\n\n Adam Johansen (Warwick University) -\n\nAsymptotic Genealogies of Sequen tial Monte Carlo Algorithms (pdf)​\n\n Sylvain Le Corff (Université P aris-Sud) -\n\nMaximum likelihood inference for large &\; sparse hidden random graphs (pdf)\n\n Bruno Nicenboim (University of Potsdam) -\n\nCog nitive models of memory processes in sentence comprehension: A case study using Bayesian hierarchical modeling (pdf)\n\n Sebastian Reich (Universi ty of Postdam) -\n\nIntroduction to data assimilation (pdf)\n\n Adeline Samson (Université Grenoble Alpes) -\n\n​​Computational statistics fo r biological models (pdf)\n\n Eric-Jan Wagenmakers (University of Amste rdam) -\n\nBayesian Inference Without Tears (pdf)\n\n Giacomo Zanella (Bo cconi University) -\n\n​Scalable Importance Tempering and Bayesian Varia ble Selection (pdf)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSPONSORS\n\n\n\n\n\n\n\ n\n\n\n\n\n \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n \n\n\n\ n\n\n\n\n \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2 018/10/image_sta-statistics_papers_formulae-x250.jpg CATEGORIES:Manifestation scientifique,Morlet Chair Semester,Morlet School END:VEVENT BEGIN:VTIMEZONE TZID:Europe/Paris X-LIC-LOCATION:Europe/Paris BEGIN:DAYLIGHT DTSTART:20180325T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST END:DAYLIGHT END:VTIMEZONE END:VCALENDAR