Results 131 to 140 of about 348,082 (329)

Propagating imprecise probabilities in Bayesian networks

Artificial Intelligence, 1996
AbstractOften experts are incapable of providing “exact” probabilities; likewise, samples on which the probabilities in networks are based must often be small and preliminary. In such cases the probabilities in the networks are imprecise. The imprecision can be handled by second order probability distributions. It is convenient to use beta or Dirichlet
openaire   +1 more source

Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling

Advanced Intelligent Systems, EarlyView.
In this article, an energy‐efficient hardware implementation of spiking‐restricted Boltzmann machines using the pseudo‐synaptic sampling (PS2) method is presented. In the PS2 method, superior area and energy efficiency over previous approaches, such as the random walk method, are demonstrated, achieving a 94.94% reduction in power consumption during on‐
Hyunwoo Kim, Suyeon Jang, Uicheol Shin, Masatoshi Ishii, Atsuya Okazaki, Megumi Ito, Akiyo Nomura, Kohji Hosokawa, Sungmin Lee, Matthew BrightSky, Sangbum Kim   +10 more
wiley   +1 more source

Apply Bayesian Inference with Normal–Normal Conjugate to Forecast Renewable Energy Generation: A Case Study of Waste-to-Energy in Taiwan

Turkish Journal of Electrical Power and Energy Systems
This paper applies Bayesian inference with normal–normal conjugate to forecast renewable energy generation. The generation forecasts a probability distribution rather than a quantitative value.
Yu-Jen Lin
doaj   +1 more source

The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks [PDF]

, 2000
Dirk Husmeier
openalex   +1 more source

Deep Learning Methods in Soft Robotics: Architectures and Applications

Advanced Intelligent Systems, EarlyView.
Soft robotics has seen intense research over the past two decades and offers a promising approach for future robotic applications. However, standard industrial methods may be challenging to apply to soft robots. Recent advances in deep learning provide powerful tools to analyze and design complex soft machines that can operate in unstructured ...
Tomáš Čakurda, Monika Trojanová, Pavlo Pomin, Alexander Hošovský   +3 more
wiley   +1 more source

Reflections on Bayesian inference and Markov chain Monte Carlo

Canadian Journal of Statistics, Volume 50, Issue 4, Page 1213-1227, December 2022., 2022
Abstract Bayesian inference and Markov chain Monte Carlo methods are vigorous areas of statistical research. Here we reflect on some recent developments and future directions in these fields. Résumé L'inférence bayésienne et les méthodes de Monte‐Carlo par chaîne de Markov sont des domaines dynamiques de la recherche statistique.
Radu V. Craiu, Paul Gustafson, Jeffrey S. Rosenthal   +2 more
wiley   +1 more source

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Advanced Intelligent Systems, EarlyView.
This work introduces random‐forest‐based interpretable generative inverse design (RIGID), a new single‐shot inverse design method for metamaterials using interpretable machine learning and Markov chain Monte Carlo sampling. Once trained on a small dataset, RIGID can estimate the likelihood of designs achieving target behaviors (e.g., wave‐based ...
Wei (Wayne) Chen, Rachel Sun, Doksoo Lee, Carlos M. Portela, Wei Chen   +4 more
wiley   +1 more source

Canadian contributions to environmetrics

Canadian Journal of Statistics, Volume 50, Issue 4, Page 1355-1386, December 2022., 2022
Abstract This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as ...
Charmaine B. Dean, Abdel H. El‐Shaarawi, Sylvia R. Esterby, Joanna Mills Flemming, Richard D. Routledge, Stephen W. Taylor, Douglas G. Woolford, James V. Zidek, Francis W. Zwiers   +8 more
wiley   +1 more source

Design of a 2D/3D Positioning System and Its Real-Time Application With Low-Cost Sensors

IEEE Access
In this article, we introduce a positioning system developed for two- and three-dimensional motion tracking. The system is based on a recursive Bayesian estimator with a dynamic naive Bayesian classifier map matching scheme.
Serkan Zobar, Mehmet Ciydem, Ozgul Salor, Jiyong Kwag, Charles K. Toth, Alper Yilmaz   +5 more
doaj   +1 more source

Predicting the Incident Cases of Emerging Infectious Disease Using a Bayesian Probability Model - China, February 2020. [PDF]

China CDC Wkly, 2020
Zhang Y, Wang X, Li Y, Wu S, Wan M, Su X, Yang S, Lai H, Jia Z, Ma J.   +9 more
europepmc   +1 more source