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:7190@i2m.univ-amu.fr DTSTART;TZID=Europe/Paris:20181126T000000 DTEND;TZID=Europe/Paris:20181130T000000 DTSTAMP:20241211T153116Z URL:https://www.i2m.univ-amu.fr/evenements/bayesian-statistics-in-the-big- data-era-morlet-chair-kerrie-mengersen/ SUMMARY:Conference (CIRM\, Luminy\, Marseille): Bayesian Statistics in the Big Data Era (Morlet Chair Kerrie Mengersen) DESCRIPTION:Conference: \n\n\n\n CIRM - Jean-Morlet Chair \n Kerrie MENGERS EN &\; Pierre PUDLO\n\nBayesian Modelling and Analysis of Big Data\n\nM odélisation bayésienne et analyse du Big data\n\n\n 2018-Semester 2 \n\n \n\n\n\n\n\n\n\n\n\nCONFERENCE\nBayesian Statistics in the Big Data Era (1 912)\nStatistiques bayésiennes à l'ère du Big Data\nDates: 26 - 30 Nove mber 2018\nPlace: CIRM (Marseille Luminy\, France)\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n SCHEDULE \n\n\n\n\n\n BOOKLET \n\n\n\n\n\n PARTICIPANTS \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\nLes méthod es bayésiennes sont aujourd'hui bien établies dans les domaines de la st atistique et du Machine Learning et sont de plus en plus appliquées au Bi g Data. Toutefois\, il existe encore des lacunes dans les connaissances su r la théorie\, la méthodologie\, le calcul et l'application des méthode s bayésiennes dans ce contexte.\nCette conférence réunira un groupe int ernational et interdisciplinaire de chercheurs et de praticiens qui partag eront leurs connaissances\, leurs recherches\, leurs défis et les possibi lités de développer et d'utiliser les statistiques bayésiennes à l'èr e des Big Data. Les résultats escomptés comprennent le transfert des con naissances\, de nouveaux réseaux de collaboration\, de nouvelles orientat ions de recherche et de nouveaux outils statistiques pour résoudre des pr oblèmes difficiles dans le monde réel.\n\n\n\n\n\n\n\n\n\n\nDESCRIPTION\ nBayesian methods are now firmly established in the fields of Statistics a nd Machine Learning and are being increasingly applied to “Big Data”. However\, there are still gaps in knowledge about the theory\, methodology \, computation and application of Bayesian methods in this context.\nThis conference will bring together an international and interdisciplinary grou p of researchers and practitioners to share insights\, research\, challeng es and opportunities in developing and using Bayesian statistics in the Bi g Data era. The anticipated outcomes include: knowledge transfer\, new col laborative networks\, new research directions and new statistical tools to address challenging problems in the real world.Themes\n\n Emerging Theor y &\; Methods: Bayesian methodology for big data modelling and analysis \n Enabling Computation: Bayesian computation for big data\n New Insight s: Applications of Bayesian analysis using big data\n Program\n\nThe prog ram will include oral presentations and substantial time for discussion af ter each presentation\, as well as poster presentations with specific sess ions for presentation and discussion.\nThe program will also include dedic ated time for research discussion\, collaboration and networking. Sessions on topics such as career pathways and mentoring will be scheduled for pos tgraduates and early career researchers.\n\n\n\n\n\n[su_spacer]\nSCIENTIFI C COMMITTEE\n\n\n Ery Arias-Castro (University of California San Diego)\n Chris Holmes (University of Oxford)\n Christian P. Robert (Université Paris-Dauphine)\n\n\n\nORGANIZING COMMITTEE\n\n\n Jean-Marc Freyermuth (A ix-Marseille Université)\n Jean-Michel Marin (Université de Montpellie r)\n Kerrie Mengersen (QUT Brisbane)\n Denys Pommeret (Aix-Marseille Un iversité)\n Pierre Pudlo (Aix-Marseille Université)\n\n\n\n\nKEYNOTES\ n\n\n Sudipto Banerjee (UCLA): High-Dimensional Bayesian Geostatistics  ​ (pdf)   - VIDEO -\n Amy Herring (Duke): Centered partition proce sses: lumping versus splitting in sparse health data    - VIDEO -\n Sy lvia Frühwirth-Schnatter (WU Vienna): Data Mining through Markov Chain M ixtures with Applications in Labor Economics and Marketing   (pdf)   - VIDEO -\n Peter Mueller (UT Austin): Scalable Bayesian Nonparametric Clu stering and Classification (pdf 1)(pdf 2)\n\n\n\n\nSPEAKERS \n\n\n Pierr e Alquier (ENSAE ParisTech) - Informed Sub-Sampling MCMC: Approximate Baye sian Inference for Large Datasets  (pdf)\n Louis Aslett (Durham Univers ity) - Privacy and Security in Pooled Bayesian Inference\n Tamara Broderi ck (MIT) - Automated Scalable Bayesian Inference via Data Summarization\n Noel Cressie (University of Wollongong)-  Inference for Spatio-Tempor al Changes of Arctic Sea Ice   (pdf)  - VIDEO - \n Marco Cuturi (EN SAE\, Paris) - Regularized Optimal Transport   (pdf)\n David B. Dunson  (Duke University) - Generalized Bayes for robust and scalable inference s from high-dimensional data   (pdf)\n Gregor Kastner (WU Vienna) - Ba yesian Inference in Many Dimensions: Examples from Macroeconomics and Fina nce  (pdf)\n Ruth King (University of Edinburgh)\n Gary Koop (Univers ity of Strathclyde) - Composite Likelihood Methods for Large Bayesian VARs with Stochastic Volatility  (pdf)\n Antonio Lijoi (Bocconi University)   - Nonparametric priors for covariate-dependent data  (pdf)\n Jean-Mic hel Marin (Université de Montpellier) - Local tree methods for classific ation   (pdf)\n Antonietta Mira (Università della Svizzera italiana a nd University of Insubria) - Bayesian dimensionality reduction via the ide ntifications of the data intrinsic dimensions\n Igor Prünster (Bocconi  University) - Hierarchies of discrete random probabilities   (pdf)\n Stéphane Robin (AgroParisTech) - Shortened Bridge Sampler: Using Determi nistic Approximations to Accelerate SMC for Posterior Sampling   (pdf)\n Heejung Shim (University of Melbourne) - Bayesian multi-scale Poisson m odels for analyses of high-throughput sequencing data in genomics   (pdf )\n\n\n Rebecca Steorts (Duke University)\n\n\n Minh-Ngoc Tran (Univer sity of Sydney) - Bayesian Computation for Big Models Big Data\n\n\n Darr en Wilkinson (Newcastle University) - A Compositional Approach to Scalabl e Bayesian Computation and Probabilistic Programming   (pdf)\n\n\n\n\n\n \n\n\n\n\nORAL PRESENTATIONS\n\n\n\n\n\n\n\n\n\n\n Atanu Bhattacharjee ( Tata Memorial Centre) - Time-Course Data Prediction for Repeatedly Measure d Gene Expression  (pdf)\n Marta Crispino (INRIA Grenoble)  - Bayesia n preference learning   (pdf)\n Christel Faes (Hasselt University Belg ium) - Accounting for residential history in disease mapping   (pdf)\n Ethan Goan (Queensland University of Technology)\n Logan Graham (Univer sity of Oxford) - Causality in Bayesian Modelling for Modern Machine Learn ing Challenges .  (pdf)\n Clara Grazian (University of Oxford)\n Zito ng Li (University of Melbourne) - Bayesian non-parametric regression for analyzing time course quantitative genetic data  (pdf)\n Benoit Liquet  (Université de Pau et des Pays de L'Adour) - Bayesian Variable Selectio n Regression Of Multivariate Responses For Group Data  (pdf)\n Jia Liu  (University of Helsinki) - Bayesian model-based spatiotemporal survey desi gn for log-Gaussian cox process  (pdf)\n Reza Mohammadi (University of Amsterdam) -  High-dimensional Bayesian inference for Graphical Models wi th Application to Brain Connectivity   (pdf)\n Ahihiko Nishimura (Univ ersity of California - Los Angeles) - Computational advances in ”large n and large p" sparse Bayesian regression for binary and survival outcomes  (pdf)\n Monica Patriche (University of Bucharest) - Equilibrium existe nce for Bayesian generalized games in choice form and applications\n Pier re Pudlo (Aix-Marseille Université) - Approximate Bayesian model choice as a Machine Learning problem  (pdf)​\n Christian P. Robert (Univers ité Paris-Dauphine) - Inference in generative models using the Wasserste in distance  (pdf)​\n Gajendra Vishwakarma (Indian Institute of Techn ology Dhanbad) - A Bayesian Approach for Dynamic Treatment Regimes in Pres ence of Competing Risk Analysis\n\n\n\n\n\n\n\n\n\nCONFERENCE POSTERS\n\n\ n\n\n\n\n\n\n\n\n Julyan Arbel (Inria Grenoble Rhône-Alpes) - Bayesian n eural networks increasingly sparsify their units with depth\n Paul-Marie Grollemund (Université de Montpellier) - Elicitation of Experts’ Knowle dge for Functional Linear Regression \n Thi Khuyen Le (Aix Marseille Univ ersité) - Connected component selection for Linear Discriminant Analysis in high dimension and applications to medical imaging \n ​Hoang Nguyen (University Carlos III of Madrid) - Variational Inference for high dimensi onal structured factor copulas\n Oluwole\, K. Oyebamiji (Lancaster Unive rsity) - Bayesian optimal weighting scheme for combining simulation ensemb le for global climate projection \n Maxime Rischard (Harvard University) - Unbiased estimation of log normalizing constants with applications to Ba yesian cross-validation\n Erlis Ruli ​(University of Padova) - Objectiv e model selection with proper scoring rules and improper priors\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 ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2 018/11/image_alea-sta-hal-01286065-hyperbolic_wavelet_bases-fig.1_extract- Autin-Claeskens-Freyermuth-x300.jpg CATEGORIES:Colloque,Morlet Chair Semester END:VEVENT BEGIN:VTIMEZONE TZID:Europe/Paris X-LIC-LOCATION:Europe/Paris BEGIN:STANDARD DTSTART:20181028T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET END:STANDARD END:VTIMEZONE END:VCALENDAR