2015/09/09 by Beate Franke, Jean-François Plante, Jean‐François Plante +13 · 1 voice · 43 citations
Computer Science · Mathematics · #Big data #Data Analysis with R #Gaussian Processes and Bayesian Inference #Statistical Methods and Inference #Statistical analysis #Statistical learning #Statistical model #Thematic map #Theme (computing) #acm:62-07 #cs.LG #msc:62-07 #stat.ML
paper · pdf · doi:10.1111/insr.12176
published in International Statistical Review 84(3), 371-389 (Wiley) · Thematic Program on Statistical Inference, Learning, and Models for Big Data, Fields Institute; 23 pages, 2 figures
arxiv created 2016/01/28 · openalex created_date 2016/06/24 · openalex publication_date 2016/06/30 · arxiv updated 2018/09/25 · openalex updated_date 2026/08/05
Summary The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.