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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZOFFSETFROM:+0900
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DTSTART:20200101T000000
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DTSTART;TZID=Asia/Seoul:20210429T120000
DTEND;TZID=Asia/Seoul:20210429T130000
DTSTAMP:20260427T231637
CREATED:20210425T180554Z
LAST-MODIFIED:20210425T180554Z
UID:4499-1619697600-1619701200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 1
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nGaussian process(GP) is a stochastic process such that the joint distribution of an arbitrary finite subset of the random variables is a multivariate normal. It plays a fundamental role in Bayesian machine learning as it can be interpreted as a prior over functions (Rasmussen and Williams\, 2006)\, hence providing a nonparametric approach to various tasks. In the first part\, I will introduce the general framework of GP and some underlying theory\, accompanied by an illustrative example of GP regression\, also known as Kringing. In the second part\, I will introduce some recent works on applying GP to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-1/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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