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Wednesday, November 1, 2017On Gaussianwidth gradient complexity and meanfield behavior of interacting particle systems and random graphsSeminar: Probability Seminar  November 1  3:104 p.m.  1011 Evans Hall Ronen Eldan, Weizmann Institute of Science The motivating question for this talk is: What does a sparse Erd\"osR\'enyi random graph, conditioned to have twice the number of triangles than the expected number, typically look like? Motivated by this question, In 2014, Chatterjee and Dembo introduced a framework for obtaining Large Deviation Principles (LDP) for nonlinear functions of Bernoulli random variables (this followed an earlier... More > Leave out estimation of variance componentsSeminar: Neyman Seminar  November 1  45 p.m.  1011 Evans Hall Patrick Kline, University of California, Berkeley We propose a general framework for unbiased estimation of quadratic forms of regression coefficients in linear models with unrestricted heteroscedasticity. Economic applications include variance component estimation in multiway fixed effects and random coefficient models. The large sample distribution of our estimator is studied in an asymptotic framework where the number of regressors grows in... More > Center for Computational Biology Seminar: Dr. Angela DePace, Associate Professor, Department of Systems Biology, Harvard Medical SchoolSeminar: Other Related Seminars  November 1  4:305:30 p.m.  125 Li Ka Shing Center Center for Computational Biology Precision and Plasticity in Animal Transcription Tuesday, November 7, 2017Seminar 217, Risk Management: Rough Heston model: Pricing, hedging and microstructural foundationsSeminar: Risk Seminar  November 7  11 a.m.1 p.m.  639 Evans Hall Speaker: Mathieu Rosenbaum, École Polytechnique Center for Risk Management Research It has been recently shown that rough volatility models, where the volatility is driven by a fractional Brownian motion with small Hurst parameter, provide very relevant dynamics in order to reproduce the behavior of both historical and implied volatilities. However, due to the nonMarkovian nature of the fractional Brownian motion, they raise new issues when it comes to the risk management of... More > Wednesday, November 8, 2017Selfavoiding polygons and walks: counting, joining and closing.Seminar  November 8  3:104 p.m.  1011 Evans Hall Alan Hammond, U.C. Berkeley Selfavoiding walk of length n on the integer lattice Z^d is the uniform measure on nearestneighbour walks in Z^d that begin at the origin and are of length n. If such a walk closes, which is to say that the walk's endpoint neighbours the origin, it is natural to complete the missing edge connecting this endpoint and the origin. The result of doing so is a selfavoiding polygon. We investigate... More > Causal inference with interfering units for cluster and population level intervention programsSeminar: Neyman Seminar  November 8  45 p.m.  1011 Evans Hall Fabrizia Mealli, University of Florence Interference arises when an individual's potential outcome Monday, November 13, 2017Loeve Prize ceremonySpecial Event: Other Related Seminars  November 13  45 p.m.  1011 Evans Hall David Aldous and Vladas Sidoravicius. Presentation of prize to Hugo DuminilCopin, and short nontechnical talk by Vladas Sidoravicius. Followed by reception 5.00  5.45 in Womens Faculty Club. Tuesday, November 14, 2017Seminar 217, Risk Management: Investor Behavior and Market DynamicsSeminar: Risk Seminar  November 14  11 a.m.1 p.m.  639 Evans Hall Speaker: John Arabadjis, State Street Center for Risk Management Research The Market is a consensual hallucination that commands attention by wielding its Invisible Hand. In this talk we will examine the ways that Adam Smith’s 250yearold appendage makes itself felt – positioning, trading, and hurting herding – and their implications for the investment process. Wednesday, November 15, 2017Probing neural circuits with shaped lightSeminar: Neyman Seminar  November 15  45 p.m.  1011 Evans Hall Na Ji, University of California, Berkeley To understand computation in the brain, one needs to understand the inputoutput relationships for neural circuits and the anatomical and functional relationships between individual neurons therein. Optical microscopy has emerged as an ideal tool in this quest, as it is capable of recording the activity of neurons distributed over millimeter dimensions with submicron spatial resolution. I will... More > Tuesday, November 28, 2017Seminar 217, Risk Management: The Futures Financing RateSeminar: Risk Seminar  November 28  11 a.m.1 p.m.  639 Evans Hall Speaker: Nicholas Gunther, UC Berkeley Center for Risk Management Research We estimate the financing rate implicit in equity index futures (“FIR”) by comparing the prices of the near and next contracts and adjusting for expected dividends and convexity. We provide a direct estimate of the FIR volatility, along with the correlation of the FIR and the underlying stock index, which are required for the convexity adjustment and the specification of confidence intervals. Our... More > Wednesday, November 29, 2017Regularity and strict positivity of densities for the nonlinear stochastic heat equationSeminar: Probability Seminar  November 29  3:104 p.m.  1011 Evans Hall Le Chen, University of Nevada, Las Vegas In this talk, I will present some recent progress in understanding the existence, regularity and strict positivity of the (joint) density of the solution to a semilinear stochastic heat equation. The talk will consists two parts. In the first part, I will show that under a mild cone condition for the diffusion coefficient, one can establish the smooth joint density at multiple points. The tool... More > Causal Inference in the Presence of InterferenceSeminar: Neyman Seminar  November 29  45 p.m.  1011 Evans Hall Michael Hudgens, UNCChapel Hill A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected depends on who... More > Thursday, November 30, 2017GraphXD Seminar: DataDriven Methods for Learning Sparse Graphical ModelsSeminar: Other Related Seminars  November 30  5:307 p.m.  1011 Evans Hall Somayeh Sojoudi, EECS, Mechanical Engineering Berkeley Institute for Data Science Learning models from data has a significant impact on many disciplines, including computer vision, medical imaging, social networks, neuroscience and signal processing. In the network inference problem, one may model the relationships between the network components through an underlying inverse covariance matrix. Learning this graphical model is often challenged by the fact that only a small... More > 

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