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Gait Type Analysis Using Dynamic Bayesian Networks [PDF]
Sensors, 2018 This paper focuses on gait abnormality type identification—specifically, recognizing antalgic gait. Through experimentation, we demonstrate that detecting an individual’s gait type is a viable biometric that can be used along with other ...
Patrick Kozlow +2 more
doaj +5 more sources
Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment [PDF]
Sensors, 2014 Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a ...
Gregory Koshmak +2 more
doaj +4 more sources
Dynamic Bayesian Networks, Elicitation, and Data Embedding for Secure Environments [PDF]
EntropySerious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities remain undisclosed. Data informing an ongoing incident are often sparse; a large proportion of relevant data only come to light ...
Kieran Drury, Jim Q. Smith
doaj +4 more sources
Feature Dynamic Bayesian Networks [PDF]
Proceedings of the 2nd Conference on Artificial General Intelligence (2009), 2008 Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments. Nevertheless, unstructured (Phi)MDPs are limited to relatively simple environments.
Hutter, Marcus
core +6 more sources
Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data [PDF]
mSystems, 2021 A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes.
Daniel Ruiz-Perez +6 more
doaj +4 more sources
Bayesian nonparametrics for Sparse Dynamic Networks
, 2016 We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point
Caron, Francois +4 more
core +3 more sources
Dynamic networks from hierarchical bayesian graph clustering. [PDF]
PLoS ONE, 2010 Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying ...
Yongjin Park +2 more
doaj +4 more sources
Polynomial-Time Algorithm for Learning Optimal BFS-Consistent Dynamic Bayesian Networks [PDF]
Entropy, 2018 Dynamic Bayesian networks (DBN) are powerful probabilistic representations that model stochastic processes. They consist of a prior network, representing the distribution over the initial variables, and a set of transition networks, representing the ...
Margarida Sousa, Alexandra M. Carvalho
doaj +2 more sources
Dynamic Bayesian Networks in system reliability analysis [PDF]
IFAC Proceedings Volumes, 2006 Today industrial systems are characterized by a set of dependencies among the components and the environment of the system. To address these difficulties, this paper presents a method for modelling and analyzing the reliability of a complex system based on Dynamic Bayesian Networks (DBN). This method allows to take into account the influence of time or
Abdeljabbar Ben Salem +2 more
openalex +4 more sources
Infinite dynamic bayesian networks [PDF]
, 2011 We present the infinite dynamic Bayesian network model (iDBN), a nonparametric, factored state-space model that generalizes dynamic Bayesian networks (DBNs).
Doshi-Velez, Finale P. +3 more
core +4 more sources