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About 4,834 papers for “Dynamic Bayesian network”

Characterization of Dynamic Bayesian Network-The Dynamic Bayesian Network as temporal network

Nabil Ghanmi, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara · Greater South Information System · 2011

In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty.We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms.Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian network.

Cited by 0Open access

Characterization of Dynamic Bayesian Network-The Dynamic Bayesian Network as temporal network

Nabil Ghanmi, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara · Greater South Information System · 2011

In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty.We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms.Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian network.

Cited by 0Open access

Dynamic Bayesian Network representation.

Paula S. Hatum (20347879), Kathryn McMahon (649523), Kerrie Mengersen (177006) et al. · Figshare · 2024

<p>Ovals represent nodes, and arrows indicate causal parent-child relationships, with the absence of a link suggesting conditional independence. Subnetworks are shown in rounded rectangles [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref077" target="_blank">77</a>]. This representation is adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref101" target="_blank">101</a>] and developed using GeNIe.</p>

Cited by 0Open access

Seagrass Dynamic Bayesian Network.

Paula S. Hatum (20347879), Kathryn McMahon (649523), Kerrie Mengersen (177006) et al. · Figshare · 2024

<p>A Bayesian Network (BN) is a class of graphical models representing the probabilistic relationships among a set of variables (or nodes) with directed arcs between them. These variables form a directed acyclic graph (DAG), and each node in the DAG has a state that varies depending on the states of other nodes [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref147" target="_blank">147</a>]. Information about those states is transmitted throughout the DAG, and as a result, inferences may be made by adding new data or evidence to the network. While BNs are suitable approaches for inferring static processes, the Dynamic Bayesian Network (DBN), a BN extension in which nodes represent variables at specific time slices, may be used to model temporally varying processes in which the state of a variable may change over time [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref148" target="_blank">148</a>]. When adding a dynamic component to a BN and creating a DBN, the characteristic processes of complex systems, including cumulative effects and feedback processes, can be captured. The DBN approach allows us to predict the resilience of a system given the temporal dynamics of the components of the ecosystem and their interactions with natural and anthropogenic stressors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref101" target="_blank">101</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref107" target="_blank">107</a>]. The overall DBN network structure is depicted with ovals representing factors (or nodes) and arrows indicating causal parent-child relationships. In this structure, a parent node (e.g., Meadow Type) exerts an influence on a child node (e.g., Location Type). Conversely, the absence of a link between nodes signifies conditional independence. Rounded rectangles denote subnetworks [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref077" target="_blank">77</a>]. Note the presence of complex interdependencies and feedback loops in the seagrass ecosystem. Node colors denote different categories: site condition (white), recovery (purple), resistance (green), environmental (blue), and population (yellow). The following symbology is used in this figure: A node with a check mark: A node is ticked when inference has been successfully executed. A curved arrow back onto itself: Denotes a link to the node’s next time slice (i.e., <i>t</i> + 1). A double-headed arrow: Indicates that arcs are heading both ways between two subnetworks. For example, a double-headed arrow between a node N and a subnetwork S means that there is at least one node in S that depends on N and that there is at least one node in S that influences N. An arrow labeled with a [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0298853#pone.0298853.ref001" target="_blank">1</a>]: Indicates a connection to a subsequent time slice (<i>t</i> + 1).</p> <p>(PDF)</p>

Cited by 0Open access

Characterization of Dynamic Bayesian Network

Nabil Ghanmy, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara · arXiv (Cornell University) · 2012

In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms. Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian network.

Cited by 8Open access

Multistream Dynamic Bayesian Network for Meeting

Alfred Dielmann, Steve Renals · 2005

This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose each meeting in a sequence of relevant phases, named meeting actions. Three feature families are extracted from multimodal recordings: prosody from individual lapel microphone signals, speaker activity from microphone array data and lexical features from textual transcripts. A statistical approach is then used to relate low-level features with a set of abstract categories.

Cited by 0

A dynamic Bayesian network model for . . .

Erick Delage, Honglak Lee, Andrew Y. Ng · 2006

Whenwelookatapicture, ourpriorknowledgeabout theworldallowsustoresolvesomeoftheambiguitiesthat areinherenttomonocularvision,andtherebyinfer 3dinformationaboutthescene. We also recognizedifferentobjects,decideontheirorientations, andidentifyhowtheyare connectedtotheirenvironment. Focusingontheproblemof autonomous3dreconstructionofindoorscenes, inthispaperwepresentadynamicBayesiannetworkmodelcapable ofresolvingsomeoftheseambiguitiesandrecovering3d informationformanyimages.Ourmodelassumesa" floorwall"geometryonthesceneandistrained torecognizethe floor-wallboundaryineachcolumnoftheimage. When theimageisproducedunderperspectivegeometry,weshow thatthismodelcanbeusedfor3dreconstructionfromasingle image. Toourknowledge,thiswasthefirstmonocular approachtoautomaticallyrecover3dreconstructionsfrom singleindoorimages.

Cited by 0

Weather Forecasting Using Dynamic Bayesian Networks Report on Dynamic Bayesian Network Learning

Matthew De Kock, Dr. Hanh Le, Dr. Mark Tadross et al. · 2013

In this paper we present an exploration of the use of Dynamic Bayesian Networks (DBNs) for the purpose of weather forecasting. The work we discuss forms part of a whole project on the subject and concerns the aspects regarding the learning and use of Dynamic Bayesian Networks. We use Southern Africa as our region of interest and have used data from a collection of weather stations from across the country. Data was available for three variables, maximum and minimum temperature and precipitation. Due to time constraints networks were only constructed and tested for forecasting maximum temperature. However, the algorithms developed apply generally to all variables. The DBN’s are constructed based on already defined intra-slice structures. The networks are built to be representations of the spatial dependencies between the stations in the region of interest. Forecasts are made by adding observed values as evidence to the first time slice of the DBN, then, using inference we can then make forecasts for the subsequent slices. Machine learning algorithms are used to construct the graphical structure and learn the required parameters. Since the intra-slice structures were already defined by an earlier part of this project the problem of constructing a DBN reduces to feature selection. A simulated annealing approach was chosen due to its ability to avoid local minima. The use of this approach is fully explored within this paper. Results show some initial promise. However, due to a limited dataset we were unable to perform a full investigation into the potential of DBN’s for weather forecast. Initial results are discussed within the context of this limited dataset and a variety of potential avenues for expanding the model are also discussed.

Cited by 0

Continuous Dynamic Bayesian Network for gene regulatory network modelling

Norhaini Baba, Mohd Saberi Mohamad, Abdul Hakim Mohamed Salleh et al. · 2014

In order to understand the underlying function of organisms, it is necessary to study the behaviour of genes in a gene regulatory network context. Several computational approaches are available for modelling gene regulatory networks with different datasets. Hence, this research is conducted to model the gene regulatory gene network using the proposed computational approach which is the Dynamic Bayesian Network. Dynamic Bayesian Network (DBN) is extensively used to construct GRNs based on its ability to handle microarray data and modelling feedback loops (cyclic regulation). The DBN approach is then extended to the continuous Dynamic Bayesian Network (cDBN) to construct a gene regulatory network with continuous data without discretization. The performance of the constructed gene networks of Saccharomyces cerevisiae were evaluated and compared with the previous works. At the end of this research, the gene networks constructed for Saccharomy cescerevisiae discovered more potential interactions between genes. Therefore, it can be concluded that the performance of the gene regulatory networks constructed using continuous dynamic Bayesian networks in this research is proven to be better because it can reveal more gene relationships as well as allowing feedback loops or cyclic regulation.

Cited by 5

Dynamic Bayesian network excluding the coagulation node.

Roberto Alberto De Blasi (10719716), Giuseppe Campagna (403571), Stefano Finazzi (229203) · Figshare · 2021

<p>The Dynamic Bayesian network without the coagulation node (C) at three time points (<i>t0</i> = the day of ICU admission; <i>t2</i> = 2nd day and <i>t7</i> = 7th day) after bootstrapping.</p>

Cited by 0Open access

Dependency parsing with dynamic Bayesian network. AAAI

Virginia Savova, Leonid Peshkin · 2005

Exact parsing with finite state automata is deemed inapropriate because of the unbounded non-locality languages overwhelmingly exhibit. We propose a way to structure the parsing task in order to make it amenable to local classification methods. This allows us to build a Dynamic Bayesian Network which uncovers the syntactic dependency structure of English sentences. Experiments with the Wall Street Journal demonstrate that the model successfully learns from labeled data.

Cited by 0

Driver Behaviour Profiling Using Dynamic Bayesian Network

James Obuhuma, Henry O. Okoyo, Sylvester Mcoyowo · International Journal of Modern Education and Computer Science · 2018

In the recent past, there has been a rapid increase in the number of vehicles and diversification of road networks worldwide. The biggest challenge now lies on how to monitor and analyse behaviours of vehicle drivers as a catalyst to road safety. Driver behaviour depends on the state and nature of the road, the state of the driver, vehicle conditions, and actions of other road users among other factors. This paper illustrates the ability of Dynamic Bayesian Networks towards determination of driving styles with respect to acceleration, cornering and braking patterns. Bayesian Networks are probabilistic graphical models that map a set of variables and their conditional dependencies. Sample test results showed that the 2-Time-slice Bayesian Network model is suitable for generation of driver profiles using only four GPS data parameters namely speed, altitude, direction and signal strength against time. The model classifies driver profiles into two sets of observations: driver behaviour and nature of operational environment. Adoption of the model could offer a cost effective, easy to implement and use solution that could find many applications in vehicle driver recruiting firms, vehicle insurance companies and transport and road safety authorities among other sectors.

Cited by 12Open access