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Impact x2 Qais

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How can we work together to promote better cultural understanding worldwide?

Qais Akbar Omar (GRS’16), a graduate student in the Creative Writing Program, has published a much-praised memoir, A Fort of Nine Towers: An Afghan Family Story. He recalls how the violence and tumult of civil war jolted his family, who, despite losing relatives, their home, and possessions, continued to nurture his wish to attend a university.

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With your help, students like Qais gain the skills they need to tell their story and give us a broader understanding of the world.

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Benjamin Shaby, UC- Berkeley

4:00 pm on Monday, February 4, 2013
5:00 pm on Monday, February 4, 2013
MCS 148
Title: Hierarchical processes for spatial extremes. Abstract: Economic and social costs brought on by the changing climate are often associated with extreme events like heat waves and intense storms. These events manifestly exhibit spatial dependence. For this reason, models for spatial fields of extreme values are an important tool for assessing risk and devising mitigation strategies. Traditional spatial models based on Gaussian processes are ill-suited to the task because they focus on the center, rather than the tail, of the distribution. In contrast, max-stable processes explicitly model the far tail. Max stable processes are the natural stochastic processes extension of classical univariate extreme value theory, and enjoy a strong theoretical motivation. Unfortunately, known spatial max-stable process models do not possess closed form joint densities, making inference, and in particular Bayesian inference, problematic. Motivated by an agricultural risk assessment application, I describe a way to sidestep this limitation by defining the process hierarchically using a finite-dimensional representation. The resulting process is max-stable, exhibits spatial dependence, has a known spatial max-stable process as a limiting case, and is amenable to true Bayesian analysis via MCMC. Furthermore, the hierarchical framework permits a number of straightforward extensions, including joint modeling of multivariate extreme value fields.