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A Hierarchical Modeling Approach to Understanding Tropospheric Ozone

• Authors : Necheles, Ella Rose Briggett; Linz, Marianna

Subjects: Applied mathematics; Atmospheric sciences

• Source: Necheles, Ella Rose Briggett. 2021. A Hierarchical Modeling Approach to Understanding Tropospheric Ozone. Bachelor's thesis, Harvard

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Topics in Bayesian Hierarchical Modeling and its Monte Carlo Computations

• Authors : Tak, Hyungsuk;

Subjects: Statistics; Physics; Astronomy and Astrophysics

• Source: Tak, Hyung Suk. 2016. Topics in Bayesian Hierarchical Modeling and its Monte Carlo Computations. Doctoral dissertation, Harvard

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Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags

• Authors : O’Brien, Jonathon J.; O’Connell, Jeremy D.; Thakurta, Sanjukta

Subjects: Article; tandem mass tags (TMT); isobaric tags for relative and absolute quantitation (iTRAQ)

• Source: O’Brien, Jonathon J., Jeremy D. O’Connell, Joao A. Paulo, Sanjukta Thakurta, Christopher M. Rose, Michael P. Weekes, Edward L. Huttlin, and Steven P. Gygi. 2017. “Compositional Proteomics:

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Estimating the Exposure–Response Relationships between Particulate Matter and Mortality within the APHEA Multicity Project

• Authors : Samoli, Evangelia; Analitis, Antonis; Touloumi, Giota

Subjects: air pollution; exposure–response; heterogeneity

• Source: Samoli, Evangelia, Antonis Analitis, Giota Touloumi, Joel Schwartz, Hugh R. Anderson, Jordi Sunyer, Luigi Bisanti, et al. 2005. Estimating the Exposureâ Response Relationships between Particulate

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A Grand Journey of Statistical Hierarchical Modelling

• Authors : Yang, Ju-Chen Justin;

Subjects: Statistics

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Generalizing the Polya-Gamma Augmented Dynamic Topic Model to Learn Ideas

• Authors : Nguyen, Andre T.

Subjects: Artificial Intelligence; Statistics; Computer Science

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Hierarchical Bayes Models with Many Instrumental Variables

• Authors : Chamberlain, Gary; Imbens, Guido W

• Source: Chamberlain, Gary and Guido W. Imbens. 1996. Hierarchical Bayes models with many instrumental variables. NBER Technical Working Paper 204: 1-26.

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