8 citations · 8 across the 4 of their papers we have counts for
4 papers
Zero-Inflated Bayesian Multi-Study Infinite Non-Negative Matrix Factorization
Blake Hansen, Dafne Zorzetto, Valeria Edefonti +1
Understanding the association between dietary patterns and health outcomes, such as the cancer risk, is crucial to inform public health guidelines and shaping future dietary interv…
Bayesian integrative factor analysis methods, with application in nutrition and genomics data
Mavis Liang, Blake Hansen, Alejandra Avalos-Pacheco +1
High-dimensional data are crucial in biomedical research. Integrating such data from multiple studies is a critical process that relies on the choice of advanced statistical models…
Bayesian Probit Multi-Study Non-negative Matrix Factorization for Mutational Signatures
Blake Hansen, Isabella N. Grabski, Giovanni Parmigiani +1
Mutational signatures are patterns of somatic mutations in tumor genomes that provide insights into underlying mutagenic processes and cancer origin. Developing reliable methods fo…
Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings
Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo +1
Factors models are routinely used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Ca…