{ "153423-21915843": { "datetime_modified":"20261007T175111", "datetime_start":"20261112T130000", "datetime_end":"20261112T180000", "has_end_time":1, "date_start":"2026-11-12", "date_end":"2026-11-12", "time_start":"13:00:00", "time_end":"18:00:00", "time_zone":"America\/Detroit", "event_title":"Eisenberg Family Depression Center 25th Anniversary Symposium & Celebration", "occurrence_title":"", "combined_title":"Eisenberg Family Depression Center 25th Anniversary Symposium & Celebration", "event_subtitle":"", "event_type":"Conference \/ Symposium", "event_type_id":"5", "description":"Join us on November 12 as we celebrate our 25th anniversary with an afternoon research symposium and reception. The day begins with the presentation of the Mike Wallace Award to Dr. John Greden for his leadership and commitment to conquering depression and related illnesses. \n\nThe symposium features a lineup of lightning talks and an interactive panel discussion focused on advances in depression and mental well-being research. We\u2019ll conclude with the presentation of the Eisenberg Research Prize to Dr. John Krystal of the Yale School of Medicine and hear more about his groundbreaking research career. \n\nStay for a reception with U-M colleagues, collaborators, and friends of the Center as we toast to this milestone. Appetizers and beverages will be served. Everyone is welcome! 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There are two main EB strategies: g-modeling, which estimates the prior by maximizing the marginal likelihood, and f-modeling, which derives posterior means directly from the empirical distribution of the observations. We show that g-modeling continues to be a valid EB procedure even when it incorrectly assumes that data are collected exogenously; its validity does not depend on the particular sampling algorithm or on whether sample sizes are endogenous. In practice, one can apply standard g-modeling techniques by acting as though the data were exogenously sampled. We extend regret guarantees from exogenous sampling to adaptively generated data. By contrast, naively applying the Tweedie formula based on the marginal density of the observed data, as in standard f-modeling, can produce biased rules under adaptive sampling. 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