Browsing by Author "Jara, Alejandro"

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    Bayesian Nonparametric Data Analysis
    (2015) Müller, Peter; Quintana Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim
    This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages
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    Clustering and feature allocation
    (2015) Müller, Peter; Quintana Quintana, Fernando; Jara, Alejandro; Hanson, Tim
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    Effectiveness of an Inactivated SARS-CoV-2 Vaccine in Chile
    (MASSACHUSETTS MEDICAL SOC, 2021) Jara, Alejandro; Undurraga, Eduardo A.; Gonzalez, Cecilia; Paredes, Fabio; Fontecilla, Tomas; Jara, Gonzalo; Pizarro, Alejandra; Acevedo, Johanna; Leo, Katherine; Leon, Francisco; Sans, Carlos; Leighton, Paulina; Suarez, Pamela; Garcia Escorza, Heriberto; Araos, Rafael
    Inactivated SARS-CoV-2 Vaccine in Chile In a national prospective cohort study involving 10.2 million participants in Chile, the effectiveness of an inactivated SARS-CoV-2 vaccine, which had been developed in China and administered in two doses 28 days apart, was estimated. Effectiveness among fully immunized persons was estimated at 65.9% for Covid-19 and at 87.5% for hospitalization, 90.3% for ICU admission, and 86.3% for death.
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    On dependent Dirichlet processes for general Polish spaces
    (2024) Iturriaga, Andres; Long, Carlos A. Sing; Jara, Alejandro
    We study Dirichlet process-based models for sets of predictor- dependent probability distributions, where the domain and predictor space are general Polish spaces. We generalize the definition of dependent Dirichlet processes, originally constructed on Euclidean spaces, to more general Polish spaces. We provide sufficient conditions under which dependent Dirichlet processes and dependent Dirichlet process mixture models have appealing properties regarding continuity (weak and strong), association structure, and support (under different topologies). The results can be easily extended to more general dependent stick -breaking processes.
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    Posterior convergence rate of a class of Dirichlet process mixture model for compositional data
    (2017) Barrientos, Andrés F.; Jara, Alejandro; Wehrhahn, Claudia