Results 51 to 60 of about 53,114 (263)

Partially-Hidden Markov Models [PDF]

, 2012
This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft” labels represent partial knowledge about the possible states at each time step and the “softness” is encoded by belief functions.
Emmanuel Ramasso, Thierry Denoeux, Noureddine Zerhouni   +2 more
openaire   +2 more sources

Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization

Advanced Science, EarlyView.
A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley   +1 more source

Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria

Journal of Applied Sciences and Environmental Management, 2016
A Hidden Markov Model (HMM) is a double stochastic process in which one of the stochastic processes is an underlying Markov chain, the other stochastic process is an observable stochastic process.
A Lawal, U.Y. Abubakar, H Danladi, A.S. Gana   +3 more
doaj   +1 more source

Multiple-regression hidden Markov model [PDF]

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
Proposes a class of hidden Markov model (HMM) called multiple-regression HMM (MR-HMM) that utilizes auxiliary features such as fundamental frequency (F/sub 0/) and speaking styles that affect spectral parameters to better model the acoustic features of phonemes.
Fujinaga, Katsuhisa, Nakai, Mitsuru, Shimodaira, Hiroshi, Sagayama, Shigeki   +3 more
openaire   +2 more sources

Hidden Markov Modeling Over Graphs

2022 IEEE Data Science and Learning Workshop (DSLW), 2022
This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference from the optimal centralized Bayesian solution is asymptotically bounded for geometrically ergodic ...
Kayaalp, Mert, Bordignon, Virginia, Vlaski, Stefan, Sayed, Ali H.   +3 more
openaire   +2 more sources

A Pan‐Methylome Framework for Population‐Scale Bacterial Epigenomics

Advanced Science, EarlyView.
A scalable quantitative framework unlocks population‐level comparative epigenomics in bacteria. By transforming site‐level data into standardized traits, this approach reconstructs methylation‐informed phylogenies and defines the core epigenome.
Bin Ma, Huimin Gong, Ran Zhuo, Zaixiao Rao, Yanxi Wan, Mingyang Wang, Zahra Zeinalzadeh, Yuan Gao, Aohan Guan, Xinrun Li, Wanting Chen, Xiliang Wang, Yun Wan, Sihua Zhang, Yuncai Xiao, Qi Huang, Paul R. Langford, Xiangru Wang, Rui Zhou, Rui Luo, Hongbo Zhou, Chengliang Zhu, Hui Jin   +22 more
wiley   +1 more source

scTIDE: Deciphering Critical Transitions Through Cell‐Perturbed Manifold Graphs and Optimal Transport Conditional Flow Matching

Advanced Science, EarlyView.
scTIDE identifies single‐cell tipping points by combining manifold‐based graph representations with optimal‐transport conditional flow matching, which preserves intrinsic topology and models distributional dynamics. It supports critical‐transition detection at individual‐cell resolution, prediction of unseen cells, and dimensionality reduction and ...
Jiayuan Zhong, Bowen Niu, Yongbo Yu, Shiyang Nie, Xuerong Gu, Pei Chen, Rui Liu   +6 more
wiley   +1 more source

Hidden semi-Markov models

Artificial Intelligence, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

Utile distinction hidden Markov models [PDF]

Twenty-first international conference on Machine learning - ICML '04, 2004
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Partially Observable Markov Decision Processes. We present a novel approach that uses a modification of the well-known Baum-Welch algorithm for learning a Hidden Markov Model (HMM)
Wierstra, D., Wiering, M.A.
openaire   +3 more sources

Transposable Element–Driven PIEZO Mutation Enhances Locust Flight in Plateau Hypoxia

Advanced Science, EarlyView.
Why transposable elements (TEs) persisted or expanded in genomes remains a mystery. Using integrated analysis of TE macro‐ and microevolution in locusts, our results showed that thousands of TE insertions promoted widespread adaptive variation. Subfamilies of candidate adaptive TEs amplified and reshaped species‐level genomic architecture.
Xuanzhao Li, Ying Liu, Longsheng Xing, Xianliang Huang, Yingming Sun, Lei Yue, Yuze Zhang, Huilong Du, Bing Chen   +8 more
wiley   +1 more source