Results 71 to 80 of about 66,463 (260)
Mathematical Methods in the Applied Sciences, EarlyView.We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés +2 more
wiley +1 more source
Sequential Bayesian Learning for Hidden Semi-Markov Models
, 2023 In this paper, we explore the class of the Hidden Semi-Markov Model (HSMM), a flexible extension of the popular Hidden Markov Model (HMM) that allows the underlying stochastic process to be a semi-Markov chain. HSMMs are typically used less frequently than their basic HMM counterpart due to the increased computational challenges when evaluating the ...
Aschermayr, Patrick +1 more
openaire +2 more sources
Structural Concrete, EarlyView.Abstract Our generation inherits this cultural heritage of historic material and historic reinforced concrete structures and thus bears a certain responsibility to preserve these historic buildings with the help of the new technologies of lifetime management, conservation concepts and the new digitalization as well as the emerging safety concepts of ...
A. Strauss
wiley +1 more source
Applied Sciences, 2020 Heart sound segmentation (HSS) is a critical step in heart sound processing, where it improves the interpretability of heart sound disease classification algorithms.
Yibo Yin, Kainan Ma, Ming Liu
doaj +1 more source
Human activity learning and segmentation using partially hidden discriminative models [PDF]
, 2005 Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance ...
Bui, Hung H. +2 more
core
Consistency of maximum likelihood estimation for some dynamical systems [PDF]
, 2014 We consider the asymptotic consistency of maximum likelihood parameter estimation for dynamical systems observed with noise. Under suitable conditions on the dynamical systems and the observations, we show that maximum likelihood parameter estimation is ...
McGoff, Kevin +3 more
core +3 more sources
DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection
IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
Hierarchical semi-markov conditional random fields for recursive sequential data [PDF]
, 2008 Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes.
Bui, Hung H. +3 more
core +2 more sources
Structure and Randomness of Continuous-Time Discrete-Event Processes
, 2017 Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process.
Crutchfield, J. P., Marzen, S. E.
core +1 more source
hsmm — An R package for analyzing hidden semi-Markov models
Computational Statistics & Data Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bulla, Jan, Bulla, Ingo, Nenadic, Oleg
openaire +3 more sources