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Auxiliary Variables for Bayesian Inference in Multi-Class Queueing Networks
[article]
2017
arXiv
pre-print
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In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases ...
Queue networks describe complex stochastic systems of both theoretical and practical interest. ...
Discussion This paper has presented a flexible approach for carrying exact Bayesian inference within known or hypothesized queueing networks. ...
arXiv:1703.03475v3
fatcat:scexcawfnjcanbaiio6p3fbn2y
A Bayesian Approach to Parameter Inference in Queueing Networks
2016
ACM Transactions on Modeling and Computer Simulation
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In this paper, we propose a methodology to estimate service demands in closed multi-class queueing networks based on Gibbs sampling. ...
The application of queueing network models to real-world applications often involves the task of estimating the service demand placed by requests at queueing nodes. ...
We define inference based on queue-length data by looking at the equilibrium state distribution of the queueing network model. ...
doi:10.1145/2893480
fatcat:c5bvsrhgkfhthozv2ksnnb5jvi
Auxiliary variables for Bayesian inference in multi-class queueing networks
2017
Statistics and computing
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In the present paper, we focus on the underlying continuoustime Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases ...
Queueing networks describe complex stochastic systems of both theoretical and practical interest. ...
Discussion This paper has presented a flexible approach for carrying exact Bayesian inference within known or hypothesized queueing networks. ...
doi:10.1007/s11222-017-9787-x
fatcat:pasy6ab7orfmjnu7qualjjwweq
Parameter and State Estimation in Queues and Related Stochastic Models: A Bibliography
[article]
2024
arXiv
pre-print
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This is an annotated bibliography on estimation and inference results for queues and related stochastic models. ...
Note that this bibliography is also a companion to our survey of parameter and state estimation in queues [20]. ...
Bayesian estimators of the traffic intensity in an M/M/1 queue are derived under a precautionary loss function. Whitt [331] : Fitting birth-and-death queueing model to data. ...
arXiv:1701.08338v3
fatcat:adyyvj5yv5g2bmaxm6cv7p7qe4
Toward real-time extraction of pedestrian contexts with stereo camera
2008
2008 5th International Conference on Networked Sensing Systems
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Index Terms-distributed camera system, stereo vision, realtime context analysis, pedestrian context, bayesian network model. ...
In this paper, we report our prototype of probabilistic inference engine that can detect contexts of individual pedestrian and groups of pedestrians. ...
The context inference by Bayesian Networks requires the heaviest processing load in our system. ...
doi:10.1109/inss.2008.4610910
fatcat:pg3mq5zzurbdlmrkk3fmymbb7i
Tracking human queues using single-point signal monitoring
2014
Proceedings of the 12th annual international conference on Mobile systems, applications, and services - MobiSys '14
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We develop two approaches in our system, one is directly feature-driven and the second uses a simple Bayesian network. ...
Our strategy extracts unique features embedded in signal traces to infer the critical time points when a person reaches the head of the queue and finishes service, and from these inferences we derive a ...
ACKNOWLEDGEMENT This work is supported in part by the National Science Foundation Grants CNS1217387, CCF1018270, CNS1040735, and CNS 0845896. ...
doi:10.1145/2594368.2594382
dblp:conf/mobisys/WangYCLGM14
fatcat:b4gl2gipq5cg5kc4opqv3fly6i
Abductive inference in Bayesian belief networks using swarm intelligence
2012
The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems
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Abductive inference in Bayesian belief networks, also known as most probable explanation (MPE) or finding the maximum a posterior instantiation (MAP), is the task of finding the most likely joint assignment ...
In this paper, a novel swarm intelligence-based algorithm is introduced that efficiently finds the k MPEs of a Bayesian network. ...
In [14] , it has been shown that abductive inference in Bayesian belief networks is NP-hard. ...
doi:10.1109/scis-isis.2012.6505074
dblp:conf/scisisis/PillaiS12
fatcat:uqkftqygwrb6xjoppwuqh6vgly
Bayesian inference for queueing networks and modeling of internet services
2011
Annals of Applied Statistics
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We also present a simple technique for selection among nested queueing models. We are unaware of any previous work that considers inference in networks of queues in the presence of missing data. ...
Such services are modeled by networks of queues, where each queue models one of the computers in the system. ...
We will not consider such adaptive schemes in this work. 3.2. Inference. We consider both Bayesian and maximum likelihood approaches to inference in this work. ...
doi:10.1214/10-aoas392
fatcat:z3b26xrzevgnlb2m4j4yivu3am
Approximating Credal Network Inferences by Linear Programming
[chapter]
2013
Lecture Notes in Computer Science
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This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. ...
An algorithm for approximate credal network updating is presented. ...
Compared to the case of Bayesian networks, inference in credal networks is considerably harder. ...
doi:10.1007/978-3-642-39091-3_2
fatcat:u4jlzvcx7rdgfojkwkyhu2y7zm
A Survey of Parameter and State Estimation in Queues
[article]
2020
arXiv
pre-print
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These include: the classical sampling approach, inverse problems, inference for non-interacting systems, inference with discrete sampling, inference with queueing fundamentals, queue inference engine problems ...
In addition to some key references mentioned here, a periodically-updated comprehensive list of references dealing with parameter and state estimation of queues will be kept in an accompanying annotated ...
Moving onto queueing networks, in [90] , Mandelbaum and Zeltyn applied the queue inference engine idea. ...
arXiv:2012.14614v1
fatcat:qd6fzn53vjf5db6arp4lg6qjie
Cognitive Congestion Control for Data Portals with Variable Link Capacity
2012
International Journal of Communications, Network and System Sciences
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Based on simulation results, the proposed method is capable of adjusting the available bandwidth by tuning the queue length, and provides a stable queue in the network. ...
In this study, a data portal is considered as an application-based network, and a cognitive method is proposed to deal with congestion in this kind of network. ...
In other words, the values of the network parameters of interest are predicted based on the observations. This prediction is done by inference, using the Bayesian network. ...
doi:10.4236/ijcns.2012.58058
fatcat:jkw6s7plozc6lpx5wllh6e67uq
Bayesian piggyback control for improving real-time communication quality
2011
2011 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR)
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Moreover, it generates few overheads to the network. ...
To achieve this goal, we propose a Bayesian loss detection mechanism based on the round trip time. ...
BAYESIAN PIGGYBACK CONTROL In this section we give an introduction to the Bayesian Inference mechanism. ...
doi:10.1109/cqr.2011.5996080
fatcat:hricieogzvfqdfcwlgs3pkii5i
Wait-Free Primitives for Initializing Bayesian Network Structure Learning on Multicore Processors
2014
2014 IEEE International Parallel & Distributed Processing Symposium Workshops
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Structure learning is a key problem in using Bayesian networks for data mining tasks but its computation complexity increases dramatically with the number of features in the dataset. ...
s (Artificial Intelligence, 137(1-2):43-90, 2002) Bayesian network structure learning algorithm. The proposed primitives are highly suitable for multithreading architectures. ...
A complementary problem to Bayesian network structure learning is Bayesian network inference. ...
doi:10.1109/ipdpsw.2014.179
dblp:conf/ipps/ChuXPP14
fatcat:dtxtoiyy4zbbdhnyc755abjpve
Continuous Time Bayesian Network Reasoning and Learning Engine
2010
Journal of machine learning research
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We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). ...
A continuous time Bayesian network (CTBN) provides a compact (factored) description of a continuoustime Markov process. ...
A continuous time Bayesian network (CTBN) consists of a set of variables, X , an initial distribution P 0 over X specified as a Bayesian network, and a graph-factored model of the dynamics of the system ...
dblp:journals/jmlr/SheltonFLLX10
fatcat:hgebgoo5dng6dgcni7ksu6txve
Identifying 802.11 Traffic From Passive Measurements Using Iterative Bayesian Inference
2012
IEEE/ACM Transactions on Networking
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The core of this classifier is an iterative Bayesian inference algorithm that we developed to obtain the maximum likelihood estimate (MLE) of these quantities. ...
In this paper, we propose a classification scheme to differentiate Ethernet and WLAN TCP flows based on measurements collected passively at the edge of a large network. ...
from the Office of Information Technology at UMass Amherst, for helping us understand the UMass network architecture, and in the installation and management of the monitoring equipment. ...
doi:10.1109/tnet.2011.2159990
fatcat:quudwg6ou5ennevlmdqijdg3jq
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