Hybrid reinforcement learning optimization of aging aware energy management and powertrain sizing in fuel cell hybrid electric vehicles. [PDF]
Sci RepMostashiri A, Montazeri-Gh M.
europepmc +1 more source
From stakeholder mapping to statistical modeling: an illustrative demonstration of end-to-end Net-Map methodology for health governance analysis. [PDF]
Arch Public HealthMihăilă BE +8 more
europepmc +1 more source
BeamCraft: Deep Reinforcement Learning-DrivenMulti-Objective Beamforming for ISAC
Dao DN, Miao Y.
europepmc +1 more source
Stochastic Actor-Oriented Models for Network Dynamics [PDF]
Annual Review of Statistics and Its Application, 2017 Stochastic Actor Oriented Models for Network Dynamics are used for the statistical analysis of longitudinal network data collected as a panel. The probability model defines an unobserved stochastic process of tie changes, where social actors add new ties or drop existing ties in response to the current network structure; the panel observations are ...
Tom A B Snijders
exaly +6 more sources
Choice modelling in social networks using stochastic actor-oriented models [PDF]
Journal of Choice Modelling, 2020 Abstract Combining choice modelling with social network analysis, we show how the stochastic actor-oriented model for the co-evolution of networks and behavior (SAOM) can be used as a powerful statistical framework to empirically analyze network-related choices.
Sebastian Pink +2 more
exaly +3 more sources
Stochastic actor oriented model with random effects
Social NetworksThe stochastic actor oriented model (SAOM) is a method for modelling social interactions and social behaviour over time. It can be used to model drivers of dynamic interactions using both exogenous covariates and endogenous network configurations, but also the co-evolution of behaviour and social interactions.
Giacomo Ceoldo +2 more
exaly +3 more sources
Visualization methods for longitudinal social networks and stochastic actor-oriented modeling [PDF]
Social Networks, 2012 Abstract As a consequence of the rising interest in longitudinal social networks and their analysis, there is also an increasing demand for tools to visualize them. We argue that similar adaptations of state-of-the-art graph-drawing methods can be used to visualize both, longitudinal networks and predictions of stochastic actor-oriented models (SAOMs),
Ulrik Brandes
exaly +3 more sources
Contemporaneous Statistics for Estimation in Stochastic Actor-Oriented Co-evolution Models [PDF]
Psychometrika, 2019 Stochastic actor-oriented models (SAOMs) can be used to analyse dynamic network data, collected by observing a network and a behaviour in a panel design. The parameters of SAOMs are usually estimated by the method of moments (MoM) implemented by a stochastic approximation algorithm, where statistics defining the moment conditions correspond in a ...
Viviana Amati +2 more
exaly +8 more sources
Assessing and accounting for time heterogeneity in stochastic actor oriented models [PDF]
Advances in Data Analysis and Classification, 2010 This paper explores time heterogeneity in stochastic actor oriented models (SAOM) proposed by Snijders (Sociological Methodology. Blackwell, Boston, pp 361-395, 2001) which are meant to study the evolution of networks. SAOMs model social networks as directed graphs with nodes representing people, organizations, etc., and dichotomous relations ...
Michael Schweinberger +2 more
exaly +6 more sources