This study proposes an important new approach to analyzing cell-count data that are often undersampled and cannot be correctly assessed with traditional statistical analyses. The presented case ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

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Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Ave. Eugenio Garza Sada 2501, Monterrey, Nuevo León 64849, Mexico ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

For whom? The events are open to all interested, within or outside of KI. The events are free of charge. The program is tailored towards users of statistics (but you don’t need to be a statistician), ...

This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including ...

Introduction: Bayesian approaches to patient phenotyping in clinical observational studies have been limited by the computational challenges associated with applying the Markov Chain Monte Carlo (MCMC ...

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