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2016년도 추계학술논문발표회 및 정기총회 안내

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작성자 관리자 작성일16-06-24 09:28 조회62회

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2016년 추계학술논문발표회 및 정기총회 안내
 
2016년도 추계학술논문발표회 및 정기총회가 통계청 통계센터(대전)에서 11월 4일(금)~5일(토) 양일간 개최될 예정입니다. 논문발표, 학회장초청강연, 특별초청강연, Tutorial 및 리셉션을 가질 예정이오니, 회원들께서는 다음 사항을 참고하시어 학술논문발표회가 성공리에 치러질 수 있도록 많은 참여와 관심을 바랍니다.
 
http://www.kss.or.kr/data/editor/1609/1890284372_1473641813.98.png
 
■ 사전등록 안내
학술논문발표회의 원활한 진행을 위하여 참가비 사전등록을 아래와 같이 받고자 합니다. 특히 회원과 비회원의 참가비가 차등 부과되오니 이점 착오 없으시기 바랍니다.
(단, 당해년도 연회비가 납부되어야 회원가로 참가비 적용됩니다.)
[등록일] 2016년 8월 29일(월) - 10월 17일(월)
[등록방법] 학회 홈페이지(http://www.kss.or.kr)에서 온라인 접수
[납부방법] 계좌입금(한국씨티은행, 186-00189-257, 예금주: 한국통계학회) 또는 학술논문발표회 웹페이지 에서 신용카드 결제
※ 참가비는 반드시 본인 이름으로 송금하여 주시기 바랍니다.
※ 당일등록에서는 현금결제만 가능하오니, 카드결제를 하셔야 하는 경우에는 사전등록기간을 이용하시기 바랍니다.
※ 사전등록 취소는 다음과 같이 가능합니다.
10월 24일(월)까지 취소시 100% 환불, 10월 31일(월)까지 취소시 50% 환불, 이후에는 취소 불가 합니다.
※ 이번 튜토리얼에서는 베이지안 통계 개요 및 Markov Chain Monte Carlo 방법과 같은 유용한 컴퓨팅 방법에 대해 소개할 예정입니다. 유익한 기회이오니, 많은 참여를 바랍니다. 특히 귀교의 대학원생들에게 널리 홍보해 주시기 바랍니다.
※ 리셉션은 사전등록 시 신청자에 한하여 입장되며(단, 비회원 학생의 경우 등록불가) 당일 등록하는 분들은 입장이 불허될 수 있습니다.  
http://www.kss.or.kr/data/editor/1608/1890284372_1472438282.79.png
 
※초청 강연 연사 소개
 
∙Peter Mueller교수(특별초청강연1)
Peter Mueller is Professor of Statistics, The University of Texas at Austin. He is a fellow of ISBA, IMS and ASA. He served as president of ISBA and chair of the Bayesian section of ASA. His research is focused on nonparametric Bayesian inference, with many applications in Bayesian biostatistics and bioinformatics, as well as innovative Bayesian clinical trial design. Recent research projects include inference on tumor heterogeneity, on inference for random graphs, random partition models and related inference problems in biostatistics. He published over 100 peer reviewed journal articles and wrote and edited several books on nonparametric Bayesian inference, Bayesian clinical trial design, and Bayesian biostatistics. He served on the scientific committees for many conferences, including many of the bi-annual workshops on nonparametric Bayesian inference, and did extensive editorial service.
 
∙Lancelot F. James(특별초청강연2)
Lancelot F. James is currently a Professor in the Business School at the Hong Kong University of Science and Technology. He has been at HKUST since 2001. Prior to that, he was an Assistant Professor in the School of Engineering at the Johns Hopkins University in Baltimore. He is well known for his work in Bayesian nonparametric statistics. Since the early 2000s, he has advocated and developed ideas related to the usage of Chinese restaurant processes, stick-breaking priors, and Pitman-Yor processes. Since that time, these colorfully named processes(the latter two named by James and his co-author Hemant Ishwaran in 2001) have played a major role in the development of intricate applications in Bayesian Statistical Machine Learning and Bayesian nonparametric methods in general, all of which are pertinent to the analysis of Big Data. Prof. James is an elected member of the International Statistical Institute. Since 2008 he is an elected Fellow of the Institute of Mathematical Statistics where he is cited for contributions to Bayesian nonparametric statistics, the development of Poisson partition calculus for Levy processes, and for dedicated service to IMS.
 
∙Patrick Groenen(학회장초청강연)
Patrick Groenen is professor in statistics and head of department at the Econometric Institute of the Erasmus University Rotterdam, the Netherlands. He is the current president of the International Association for Statistical Computing. Together with Ingwer Borg, he is a coauthor of a standard text book on multidimensional scaling with more than 1,700 cites on the Web of Science and and 4,500 on Google Scholar. His core research interests lies in computational exploratory methods in data science. He wrote articles on visualisation methods(multiple correspondence analysis, multidimensional scaling, biplots, biadditive models), optimization in statistics(MM algorithms, majorisation, constrained optimisation, least-squares, global optimisation, meta heuristics), unsupervised classification(clustering and fuzzy clustering), and applications in management, economics, biostatistics, genetics, and psychology. His current research interests includes supervised classification methods such as binary and multiclass support vector machines.
 
※Tutorial 강연 내용 소개
 
Bayesian statistics is an alternative approach to statistical inference, where Bayes rule is used to combine the prior probability of unknown model components before observing data with the likelihood of data in order to produce the corresponding posterior probability after observing data. Advanced computing power that did not exist even 20 or so years ago has continued to popularize Bayesian statistics in applied fields at an ever increasing rate. Indeed, it would not be possible to fit highly structured statistical models that are now prevalent in biological, physical, engineering, and social sciences without the recent advances in statistical computing. This tutorial will overview Bayesian statistics and introduce some useful computing methods such as Markov chain Monte Carlo methods that we frequently encounter in Bayesian statistics.
 
 
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