As clinical drug development becomes more complex and resource-intensive, the FDA’s recent draft guidance on the use of Bayesian statistical methods in clinical trials signals a move toward more ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Erik Steiger discusses the operational pain ...

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

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

Abstract: Probabilistic/stochastic computations form the backbone of autonomous systems and classifiers. Recently, biomedical applications of probabilistic computing ...

The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...

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 ...

Many studies have investigated the contributions of vision, touch, and proprioception to body ownership, i.e., the multisensory perception of limbs and body parts as our own. However, the ...

Differential equations (DEs) are commonly used to model the temporal evolution of biological systems, but statistical methods for comparing DE models to data and for parameter inference are relatively ...

Data from human subjects as well as animals show that working memories are associated with a sense of uncertainty. Indeed, a sense of uncertainty is what allows an observer to properly weigh new ...