Results 81 to 90 of about 7,217 (228)

Maximum likelihood estimation for hidden semi-Markov models

Comptes Rendus. Mathématique, 2006
In this Note we consider a discrete-time hidden semi-Markov model and we prove that the nonparametric maximum likelihood estimators for the characteristics of such a model have nice asymptotic properties, namely consistency and asymptotic normality.
Barbu, Vlad, Limnios, Nikolaos
openaire   +2 more sources

Medical Knowledge Integration Into Reinforcement Learning Algorithms for Dynamic Treatment Regimes

International Statistical Review, EarlyView.
Summary The goal of precision medicine is to provide individualised treatment at each stage of chronic diseases, a concept formalised by dynamic treatment regimes (DTR). These regimes adapt treatment strategies based on decision rules learned from clinical data to enhance therapeutic effectiveness.
Sophia Yazzourh, Nicolas Savy, Philippe Saint‐Pierre, Michael R. Kosorok   +3 more
wiley   +1 more source

Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data

IEEE Access
We consider the detection and characterization of brain state transitions based on ongoing electroencephalography (EEG). Here, a brain state represents a specific brain dynamical regime or mode of operation that produces a characteristic quasi-stable ...
Nelson J. Trujillo-Barreto, David Araya Galvez, Aland Astudillo, Wael El-Deredy   +3 more
doaj   +1 more source

Conversational AI Agents: The Effect of Process and Outcome Variation on Anthropomorphism and Trust

Information Systems Journal, EarlyView.
ABSTRACT Organisations increasingly deploy conversational AI agents (CAs) in agentic roles where behavioural variations are inevitable. Prior work often conflates two distinct forms of variation: outcome variation (where success fluctuates) and process variation (where the path to completion varies).
Kambiz Saffarizadeh, Mark Keil
wiley   +1 more source

A New State Recognition and Prognosis Method Based on a Sparse Representation Feature and the Hidden Semi-Markov Model

IEEE Access, 2020
Equipment degradation state recognition and prognosis are considered two significant parts of a prognostics and health management (PHM) system that help to reduce downtime and decrease economic losses.
Yun-Fei Ma, Xisheng Jia, Qiwei Hu, Huajun Bai, Chiming Guo, Shuangchuan Wang   +5 more
doaj   +1 more source

Breeding 5.0: Artificial intelligence (AI)‐decoded germplasm for accelerated crop innovation

Journal of Integrative Plant Biology, EarlyView.
ABSTRACT Crop breeding technologies are vital for global food security. While traditional methods have improved yield, stress tolerance, and nutrition, rising challenges such as climate instability, land loss, and pest pressure now demand new solutions.
Jiayi Fu, Shouzhi Zheng, Longjiang Fan, Xiaoming Zheng, Qian Qian   +4 more
wiley   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, EarlyView.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen, Tord Johan Espe, Kristoffer Grude, Morten Risstad, Rickard Sandberg   +4 more
wiley   +1 more source

Research on Residual Life Prediction for Electrical Connectors Based on Intermittent Failure and Hidden Semi-Markov Model

Applied Sciences, 2018
Based on the dynamic properties of electrical connector intermittent failure, the model and methods for residual life prediction for electrical connectors are studied in this paper.
Qian Li, Kehong Lv, Jing Qiu, Guanjun Liu   +3 more
doaj   +1 more source

A perspective on automated rapid eye movement sleep assessment

Journal of Sleep Research, Volume 34, Issue 2, April 2025.
Summary Rapid eye movement sleep is associated with distinct changes in various biomedical signals that can be easily captured during sleep, lending themselves to automated sleep staging using machine learning systems. Here, we provide a perspective on the critical characteristics of biomedical signals associated with rapid eye movement sleep and how ...
Mathias Baumert, Huy Phan
wiley   +1 more source

Automation and Augmentation in Theological Perspective

Modern Theology, EarlyView.
Abstract AI enables forms of automation that threaten unemployment and deskilling, eliminating important opportunities for the development of virtue. The concomitant loss of virtue and meaningful employment makes it a theological problem from the perspective of Catholic social teaching and theological anthropology.
Paul Scherz
wiley   +1 more source