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SUMMARY:Aude Hofleitner\, Facebook
DESCRIPTION:“Inferring and understanding travel and migration movements at a global scale” \nAbstract: Despite extensive work on the dynamics and outcomes of large-scale migrations\, timely and accurate estimates of population movements do not exist. While censuses\, surveys\, and observational data have been used to measure migration\, estimates based on these data sources are constrained in their inability to detect unfolding migrations\, and lack temporal and demographic detail. In this study\, we present a novel approach for generating estimates of migration that can measure movements of particular demographic groups across country lines. \nSpecifically\, we model migration as a function of long-term moves across countries using aggregated Facebook data. We demonstrate that this methodological approach can be used to produce accurate measures of past and ongoing migrations – both short-term patterns and long-term changes in residence. Several case studies confirm the validity of our approach\, and highlight the tremendous potential of information obtained from online platforms to enable novel research on human migration events. \nIf you are interested in meeting with or joining the speaker for lunch\, please send email to Seminars@ccpr.ucla.edu
URL:https://ccpr.ucla.edu/event/aude-hofleitner-facebook/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events
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ORGANIZER;CN="CCPR Seminars":MAILTO:seminars@ccpr.ucla.edu
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SUMMARY:Bayesian Statistical Modeling Using Stan
DESCRIPTION:Daniel Lee\nJune 23\, 2015\n10:00 AM-12:00 PM\n\n4240 Public Affairs Building\n\n\n\n\n\nStan is an open-source\, Bayesian inference tool with interfaces in R\, Python\, Matlab\, Julia\, Stata\, and the command line. Users write statistical models in a high-level statistical language. The default Bayesian inference algorithm is the no-U-turn sampler (NUTS)\, an auto-tuned version of Hamiltonian Monte Carlo. Stan was developed to address the speed and scalability issues of existing Bayesian inference tools. The goal of the workshop is the practical application of Stan to different models starting with ordinary linear regression and ending with more complex models such as generalized linear mixed and hierarchical models.
URL:https://ccpr.ucla.edu/event/bayesian-statistical-modeling-using-stan/
LOCATION:4240 Public Affairs Bldg
CATEGORIES:CCPR Workshop,CSS Events
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