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1/1/0001 - ptimisation de  F ormes) is a bi-annual international scientific conference which gathers junior as well as senior researchers dealing with mathematical and numerical issues in the fields of inverse problems, control and shape optimization. Important information about registration: The registration fees (which are rather staying fees) include everything, from the housing on the site of the conference, the meals, to the transport to and fro Grenoble train station. Early bird registration fees (before April 15th, tax included): permanent researcher: 300 euros; student: 252 euros. Late registration (after  April 15th, tax included): permanent researcher: 360 euros; student; 252 euros.  Payment can be achieved either by credit card (please when going through the registration pages, proceed till the very end of the secure payment process), or by money order: in this case, your registration will only be complete upon reception of the payment (follow the link `payment' above). Important infor

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5/31/2016 - Shuttle from Grenoble train station leaving at 11:00 AM Shuttle from Grenoble train station leaving at 11:00 AM ›12:30 (1h30)

1/1/0001 - Exact Geodesic PCA in wasserstein space . Summary: Principal Component Analysis (PCA) in a linear space is certainly the most widely used approach in multivariate statistics to summarize efficiently the information in a data set. In this talk, we are concerned by the statistical analysis of data sets whose elements are histograms with support on the real line. For the purpose of dimension reduction and data visualization of variables in the space of histograms, it is of interest to compute their principal modes of variation around a mean element. However, since the number, size or locations of significant bins may vary from one histogram to another, using PCA in an Euclidean space is not an appropriate tool. In this work, an histogram is modeled as a probability density function (pdf) with support included in an interval of the real line, and the Wasserstein metric is used to measure the distance between two histograms. In this setting, the variability in a set of histograms can be ana

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