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Learning the Structure of Dynamic Probabilistic Networks [article]

Nir Friedman, Kevin Murphy, Stuart Russell
2013 arXiv   pre-print
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data.  ...  We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden.  ...  Acknowledgments We thank Jeff Forbes and Nikunj Oza for their help in getting the training data for the driving domain.  ... 
arXiv:1301.7374v1 fatcat:gdkbewxadrgtlilzk7x7qcxm7q

Challenges on Probabilistic Modeling for Evolving Networks [article]

Jianguo Ding, Pascal Bouvry
2013 arXiv   pre-print
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from  ...  This paper presents a survey on probabilistic modeling for evolving networks and identifies the new challenges which emerge on the probabilistic models and optimization strategies in the potential application  ...  The second type of network dynamics, named dynamics of the network, governs the changes in the network structure and evolution of the structure.  ... 
arXiv:1304.7820v2 fatcat:qvtskgtvpvh2npqbyjlghyu774

Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Pedro Shiguihara, Alneu de Andrade Lopes, David Mauricio
2021 IEEE Access  
Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii)  ...  INDEX TERMS Dynamic Bayesian Networks, dynamic probabilistic graphical models, literature review, systematic literature review.  ...  M09 Temporal Qualitative Probabilistic Networks Learning [47] 2010 Method for learning Temporal Qualitative Probabilistic Networks (TQPN) from time series, using DBN learning methods based on Markov  ... 
doi:10.1109/access.2021.3105520 fatcat:6tgkrpzou5d7dkkgiasvdo7z7m

Local Learning in Probabilistic Networks with Hidden Variables

Stuart J. Russell, John Binder, Daphne Koller, Keiji Kanazawa
1995 International Joint Conference on Artificial Intelligence  
explicit representations of causal structure human experts can easily contribute pnor knowledge to the training process, thereby significantly improving the learning rate Adaptive probabilistic networks  ...  alio extend the method to networks with intensionally represented distributions, including networks with continuous variables and dynamic probabilistic networks Because probabilistic networks provide  ...  of Equation 5 is in learning dynamic probabilistic networks (DPNs) i e , networks that represent a temporal stochastic process Such networks are typically divided into time slices where the nodes at each  ... 
dblp:conf/ijcai/RussellBKK95 fatcat:qtwcj5tvyrdc7cuonhpdpdq5da

Dynamic Deep Forest: An Ensemble Classification Method for Network Intrusion Detection

Bo Hu, Jinxi Wang, Yifan Zhu, Tan Yang
2019 Electronics  
It uses cascade tree structure to strengthen the representation learning ability.  ...  However, due to the diversity of intrusion types, the representation learning ability of the existing models is still deficient, which limits the further improvement of the detection performance.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics8090968 fatcat:d5jhhbrb3ney3ms332rgtv2mu4

AI Centered on Scene Fitting and Dynamic Cognitive Network [article]

Feng Chen
2020 arXiv   pre-print
principle of cognitive network optimization is proposed, and the basic framework of Cognitive Network Learning algorithm (CNL) is designed that structure learning is the primary method and parameter learning  ...  It also discusses the concrete scheme named Dynamic Cognitive Network model (DC Net).  ...  With facing the scenes of open domains, significance probabilities, heterogeneous and dynamic network structure, it is called the cognitive probabilistic model.  ... 
arXiv:2010.04551v1 fatcat:ivvxjx3ggrbmxjpwmprlbsfvcq

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics [article]

Ke Alexander Wang, Danielle Maddix, Yuyang Wang
2021 arXiv   pre-print
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.  ...  We present GOPHER, a method that combines the inductive bias of graph neural networks with neural ODEs to capture the intrinsic local continuous-time dynamics of our probabilistic forecasts.  ...  To capture the equivariant local dynamics of our forecast p(t), we propose GOPHER, a model that learns a neural ODE [4] with graph neural network (GNN) [26] dynamics.  ... 
arXiv:2112.09964v1 fatcat:tshojwojaffordbwpaluoaail4

Learning probabilistic models of connectivity from multiple spike train data

Debprakash Patnaik, Srivatsan Laxman, Naren Ramakrishnan
2010 BMC Neuroscience  
Efficient Learning: Excitatory network assumption allows the use of connect fast frequent episode mining algorithms to learn network structures.  ...  Synfire Chains A volley of firing in one group of neurons causes next group to fire and activity propagates over the network. The gray boxes show the MEA view of the activity.  ... 
doi:10.1186/1471-2202-11-s1-p171 pmcid:PMC3090878 fatcat:jnuabs6mkbgivinovq4hgtp56u

Proposing the Deep Dynamic Bayesian Network as a Future Computer Based Medical System

Caoimhe M. Carbery, Adele H. Marshall, Roger Woods
2016 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)  
ACKNOWLEDGMENT The authors would like to express their thanks to the Engineering and Physical Sciences Research Council (EPSRC) for funding this research.  ...  Index Terms-probabilistic graphical model; deep learning; dynamic Bayesian network; medical systems I.  ...  The benefits of including a dynamic component are vast, with one important benefit Fig. 2 . 2 An example of the structure of a dynamic Bayesian network with three variables X 1 , X 2 , X 3 across three  ... 
doi:10.1109/cbms.2016.70 dblp:conf/cbms/CarberyMW16 fatcat:tj5ny5zhl5b6fduycinbmwh35i

Structure evolution of dynamic Bayesian network for traffic accident detection

Ju-Won Hwang, Young-Seol Lee, Sung-Bae Cho
2011 2011 IEEE Congress of Evolutionary Computation (CEC)  
Effectiveness of the generated structure of dynamic Bayesian network is evaluated in terms of evolution process and the accuracy in a domain of the traffic accident detection.  ...  Keywords-structure of dynamic Bayesian network; Bayesian network, evolution I. 978-1-4244-7833-0/11/$26.00 ©2011 IEEE  ...  ACKNOWLEDGMENT This work was supported by the IT R&D program of MKE/KEIT (10033807, Development of context awareness based on self learning for multiple sensors cooperation).  ... 
doi:10.1109/cec.2011.5949815 dblp:conf/cec/HwangLC11 fatcat:k5ppotsenremleitms6dekdo7a

Special feature: probabilistic graphical models and its applications to biomedical informatics—part 1

Joe Suzuki, Brandon Malone
2017 Behaviormetrika  
Machine learning and artificial intelligence approaches are among the most promising techniques for extracting useful biological signals from the noise inherent in these technologies.  ...  This special feature focuses on probabilistic graphical modeling approaches to biomedical informatics. A wide variety of problems remain open in this domain.  ...  learning of structures from observational data, local structural learning approaches, and the active learning for optimal designs of intervention in their article ''Structural Learning of Causal Networks  ... 
doi:10.1007/s41237-017-0015-y fatcat:w54xcykrkfcu3n5dulvcsydsgi

Probabilistic computation underlying sequence learning in a spiking attractor memory network

Philip Tully, Henrik Lindén, Matthias H Hennig, Anders Lansner
2013 BMC Neuroscience  
We demonstrate the feasibility of our model using network simulations of integrate-and-fire neurons, and find that the ability to learn sequences depends on the specific structure of the inhibitory microcircuitry  ...  We consider the task of generating and learning spatiotemporal spike patterns in the context of an attractor memory network, in which each memory is stored in a distributed fashion represented by increased  ...  We demonstrate the feasibility of our model using network simulations of integrate-and-fire neurons, and find that the ability to learn sequences depends on the specific structure of the inhibitory microcircuitry  ... 
doi:10.1186/1471-2202-14-s1-p236 pmcid:PMC3704468 fatcat:ewvdqqcmerdyvhsdd2ytxfecla

Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model [article]

Ahmadreza Ahmadi, Jun Tani
2017 arXiv   pre-print
of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets.  ...  The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms.  ...  Murata and colleagues experimented with the degree of the initial state dependency in learning to imitate probabilistic sequences, and examined how the internal dynamic structure develops differently in  ... 
arXiv:1706.10240v2 fatcat:fxqacpushngmfaytidigev3u5y

Dynamic Data Feed to Bayesian Network Model and SMILE Web Application [chapter]

Nipat Jongsawat, Anunucha, Wichian
2010 Bayesian Network  
SMILE (Structural Modeling, Inference, and Learning Engine) is a fully platform independent library of functions implementing graphical probabilistic and decisiontheoretic models, such as Bayesian networks  ...  The structure of a Bayesian network is a graphical, qualitative illustration of the interactions among the set of variables that it models.  ... 
doi:10.5772/10071 fatcat:5abhmf5zrrh6femwlk5rsrfh3y

Causal probabilistic input dependency learning for switching model in VLSI circuits

Nirmal Ramalingam, Sanjukta Bhanja
2005 Proceedings of the 15th ACM Great Lakes symposium on VLSI - GLSVSLI '05  
In this work, we model the input-space by a causal graphical probabilistic model that encapsulates the dependencies in inputs in a compact, minimal fashion and also allows for instantiations of the vector-space  ...  Switching model captures the data-driven uncertainty in logic circuits in a comprehensive probabilistic framework.  ...  The learned structure of the Bayesian network represents the graphical structure in the input data. The learned structure can now be used to generate any number of vectors.  ... 
doi:10.1145/1057661.1057689 dblp:conf/glvlsi/RamalingamB05 fatcat:pvke5qh2orhobkmmz25x5246w4
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