In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...

Abstract: In this work, we have developed a variational Bayesian inference theory of elasticity, which is accomplished by using a mixed Variational Bayesian inference Finite Element Method (VBI-FEM) ...

Abstract: Conventional neural network-based machine learning algorithms often encounter difficulties in data-limited scenarios or where interpretability is critical. Conversely, Bayesian ...

Bayesian inference offers a principled framework for estimating the key parameters of queueing models by combining prior knowledge with observed data. In service and manufacturing environments, ...

Empirical investigation requires dealing with fundamental uncertainty. In experimental psychology, research questions are often addressed using Null Hypothesis Significance Testing (NHST), an approach ...

The course is structured in four main parts, covering the full Bayesian workflow: from probabilistic reasoning to advanced modeling. BAYESIANLEARNING/ │ ├── PART-I/ │ ├── theory/ │ │ └── ...

Inference is rapidly emerging as the next major frontier in artificial intelligence (AI). Historically, the AI development and deployment focus has been overwhelmingly on training with approximately ...

Easily build Bayesian models from parts, abstract away the boilerplate, and tweak priors as you wish. Inspiration from Keras and Tensorflow Probability, but made specifically for Numpyro + Jax.

Dormancy is a widespread bet-hedging strategy across taxa, enabling organisms to survive natural and anthropogenic disturbances. It fundamentally alters eco-evolutionary processes, including ...

A research team at Tohoku University's Advanced Institute for Materials Research (WPI-AIMR) has developed a new technique to rapidly and accurately determine the charge state of electrons confined in ...