Results 81 to 90 of about 937,585 (275)

Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

Entropy, 2022
The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user ...
Juan Sun, Shubin Zhang, Changsong Yang, Liang Huang   +3 more
doaj   +1 more source

Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making [PDF]

arXiv, 2020
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning. In this paper, we propose a novel Forward-Backward Learning procedure to test MA in sequential decision making. The proposed test does not assume any parametric form on the joint distribution of the observed data and plays an important role for identifying ...
arxiv  

Decentralized control of partially observable Markov decision processes [PDF]

52nd IEEE Conference on Decision and Control, 2013
Markov decision processes (MDPs) are often used to model sequential decision problems involving uncertainty under the assumption of centralized control. However, many large, distributed systems do not permit centralized control due to communication limitations (such as cost, latency or corruption).
Alborz Geramifard, Mykel J. Kochenderfer, Christopher Amato, N. Kemal Ure, Girish Chowdhary   +4 more
openaire   +1 more source

Monte-Carlo-based partially observable Markov decision process approximations for adaptive sensing

International Workshop on Discrete Event Systems, 2008
Adaptive sensing involves actively managing sensor resources to achieve a sensing task, such as object detection, classification, and tracking, and represents a promising direction for new applications of discrete event system methods.
Edwin K. P. Chong, Chris Kreucher, Alfred O. Hero   +2 more
semanticscholar   +1 more source

A Multi‐Objective Molecular Generation Method Based on Pareto Algorithm and Monte Carlo Tree Search

Advanced Science, EarlyView.
Pareto Monte Carlo Tree Search Molecular Generation (PMMG), a molecular generation approach leveraging Monte Carlo Tree Search (MCTS) and Pareto algorithm, efficiently explores the Pareto front in high‐dimensional objective spaces for multi‐objective drug design.
Yifei Liu, Yiheng Zhu, Jike Wang, Renling Hu, Chao Shen, Wanglin Qu, Gaoang Wang, Qun Su, Yuchen Zhu, Yu Kang, Peichen Pan, Chang‐Yu Hsieh, Tingjun Hou   +12 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

DPImpute: A Genotype Imputation Framework for Ultra‐Low Coverage Whole‐Genome Sequencing and its Application in Genomic Selection

Advanced Science, EarlyView.
DPImpute is a two‐step pipeline that outperforms existing tools in whole‐genome SNP imputation, particularly under conditions of ultra‐low coverage sequencing, small sample sizes, and limited references. It enables precise imputation for single blastocyst cells, supporting genomic selection at the pre‐implantation stage.
Weigang Zheng, Wenlong Ma, Zhilong Chen, Chao Wang, Tao Sun, Wenjun Dong, Wenjing Zhang, Song Zhang, Zhonglin Tang, Kui Li, Yunxiang Zhao, Yuwen Liu   +11 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

Decision-making models on perceptual uncertainty with distributional reinforcement learning

Green Energy and Intelligent Transportation, 2023
Decision-making for autonomous vehicles in the presence of obstacle occlusions is difficult because the lack of accurate information affects the judgment.
Shuyuan Xu, Qiao Liu, Yuhui Hu, Mengtian Xu, Jiachen Hao   +4 more
doaj  

On Biologically Inspired Stochastic Reinforcement Deep Learning: A Case Study on Visual Surveillance

IEEE Access, 2019
Here, we present a biologically inspired visual network (BIVnet) for image processing tasks. The proposed model possesses similarities with its neural counterpart and is trained by a stochastic algorithm which employs a partially observable Markov ...
Nadine Hajj, Mariette Awad
doaj   +1 more source