Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
Photos of Malaysia's health minister Dzulkefly Ahmad and a former government official have been manipulated with AI and shared in social media posts advertising an unauthorised joint cream -- which ...
Nested sampling (NS) has emerged as a powerful tool for exploring thermodynamic properties in materials science. However, its efficiency is often hindered by the limitations of Markov chain Monte ...
Faculty of Biotechnology, Federal University of Pará, Belém, PA 66075-110, Brazil Laboratory of Neurophysiology Eduardo Oswaldo Cruz, Institute of Biological Science, Federal University of Pará, Belém ...
Neuroscience has witnessed a surge in data generation due to advancements in experimental techniques like electrophysiology, imaging, and genomics. To gain deeper insights into the brain's structure ...
Version 2 has the function for model selection with WBIC (widely applicable Bayesian Information Criterion). Watanabe, S., Ishikawa, T., Nakamura, Y., & Yokota, Y. (in prep.). Model selection for the ...
emcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has ...
Inference of gene flow using genomic data requires powerful methods as the process of coalescent, migration, and mutation is highly stochastic. However, it is challenging to implement the multispecies ...
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