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Upcoming Events › STOR Colloquium

January 2018

Mariana Olvera-Cravioto, University of California, Berkeley

January 10, 2018 @ 2:30 pm - 3:30 pm

Efficient simulation for branching recursions   A variety of problems in science and engineering, ranging from population and statistical physics models to the study of queueing systems, computer networks and the internet, lead to the analysis of branching distributional equations. The solutions to these equations are not in general analytically tractable, and hence need to be computed numerically. This talk discusses a simulation algorithm known as “Population Dynamics”, which is designed to produce a pool of identically distributed observations having…

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CANCELLED: Jie Ding, Harvard University

January 17, 2018 @ 2:30 pm - 3:30 pm

Some New Foundational Principles and Fast Algorithms in Data Analytics   Rapid developments in communications, networking, AI robots, 3D printing, genomics, blockchain, novel materials, and powerful computation platforms are rapidly bringing data-generating people, processes and devices together. The interactions between data analytics in multiple regimes (sparse, panel, big data, etc.) and other fields are exciting because the tools that are being invented now may enable new, faster and semi-automated methods of scientific discovery. These, in turn, might further amplify the…

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STOR Colloquium: Mikael Kuusela, SAMSI and UNC-Chapel Hill

January 19, 2018 @ 2:30 pm - 3:30 pm

Locally stationary spatio-temporal interpolation of Argo profiling float data   Argo floats measure sea water temperature and salinity in the upper 2,000 m of the global ocean. The statistical analysis of the resulting spatio-temporal data set is challenging due to its nonstationary structure and large size. I propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable nonstationary anomaly fields without the…

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STOR Colloquium: Jason Xu, UCLA

January 22, 2018 @ 2:30 pm - 3:30 pm

Enabling likelihood-based inference for complex and dependent data   The likelihood function is central to many statistical procedures, but poses challenges in classical and modern data settings. Motivated by emergent cell lineage tracking experiments to study blood cell production, we present recent methodology enabling likelihood-based inference for partially observed data arising from continuous-time stochastic processes with countable state space. These computational advances allow principled procedures such as maximum likelihood estimation, posterior inference, and expectation-maximization (EM) algorithms in previously intractable data…

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STOR Colloquium: Shizhe Chen, Columbia University

January 24, 2018 @ 2:30 pm - 3:30 pm

Learning the Connectivity of Large Sets of Neurons   New techniques in neuroscience have opened the door to rich new data sets of neural activities. These data sets shed light on the computational foundation of the brain, i.e., neurons and synapses. However, these data also present unprecedented challenges: novel statistical theory and methods are required to model neural activities, and well-designed experiments are needed to collect informative data. In this talk, we take on the task of learning connectivity among…

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STOR Colloquium: Robin Gong, Harvard University

January 26, 2018 @ 2:30 pm - 3:30 pm

Bayes is sensitive. Is imprecise probability more sensible?   Bayes is prized as principled and coherent, but its quality of inference is sensitive to prior and model misspecifications. Imprecise probability (IP) allows for the flexible expression of partially deficient probabilistic information. In our quest for minimal-assumption inference, is IP a more promising alternative to Bayes?   In this talk, I showcase the power of IP with an application of the Dempster-Shafer theory of belief functions to the prior-free estimation of…

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STOR Colloquium: Sara Algeri, Imperial College London

January 29, 2018 @ 2:30 pm - 3:30 pm

Testing One Hypothesis Multiple Times   The identification of new rare signals in data, the detection of a sudden change in a trend, and the selection of competing models, are some among the most challenging problems in statistical practice. In this talk I discuss how these challenges can be tackled using a test of hypothesis where a nuisance parameter is present only under the alternative, and how a computationally efficient solution can be obtained by Testing One Hypothesis Multiple times…

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February 2018

STOR Colloquium: Jie Ding, Harvard University

February 2, 2018 @ 2:30 pm - 3:30 pm

Some New Foundational Principles and Fast Algorithms in Data Analytics   Rapid developments in communications, networking, AI robots, 3D printing, genomics, blockchain, novel materials, and powerful computation platforms are rapidly bringing data-generating people, processes and devices together. The interactions between data analytics in multiple regimes (sparse, panel, big data, etc.) and other fields are exciting because the tools that are being invented now may enable new, faster and semi-automated methods of scientific discovery. These, in turn, might further amplify the…

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STOR Colloquium: Jason Klusowski, Yale University

February 5, 2018 @ 2:30 pm - 3:30 pm

Counting connected components and motifs of large graphs via graph sampling   Learning properties of large graphs from samples has been an important problem in statistical network analysis since the early work of Goodman (1949) and Frank (1978). We revisit a problem formulated by Frank of estimating the number of connected components in a large graph based on the subgraph sampling model, in which we randomly sample a subset of the vertices and observe the induced subgraph. The key question…

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STOR Colloquium: Kai Zhang, UNC-Chapel Hill

February 19, 2018 @ 2:30 pm - 3:30 pm

Kai Zhang University of North Carolina, Chapel Hill BET on Independence   We study the problem of nonparametric dependence detection. Many existing methods suffer severe power loss due to non-uniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat) and binary expansion testing (BET), which examine dependence through a novel binary expansion filtration approximation of the copula. Through a Hadamard transform,…

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