Abstract: This tutorial paper provides a short introduction to selected aspects of sensor data fusion by discussing characteristic examples. We consider three cases when fusion of sensor data is ...

Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences ...

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

Welcome to the repository of tutorials on how to do Bayesian Statistics using Julia and Turing. Tutorials are available at storopoli.io/Bayesian-Julia. Bayesian ...

机器学习主要分为监督学习、无监督学习和强化学习三大分支。强化学习在人工智能应用中极具潜力，它通过不断改进和微调可能的解决方案的渐进过程来解决实际问题。这种体现适应能力的渐进方法适用于大多数事件持续且意外发生的现实世界。此外，数据量 ...

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 ...

Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of ...

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2018. Cells are the basic units of life, yet their architecture and ...

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material ...