3,796 research outputs found
Partition strategies for incremental Mini-Bucket
Los modelos en grafo probabilĂsticos, tales como los campos aleatorios de
Markov y las redes bayesianas, ofrecen poderosos marcos de trabajo para la
representaciĂłn de conocimiento y el razonamiento en modelos con gran nĂşmero
de variables. Sin embargo, los problemas de inferencia exacta en modelos de
grafos son NP-hard en general, lo que ha causado que se produzca bastante
interĂŠs en mĂŠtodos de inferencia aproximados.
El mini-bucket incremental es un marco de trabajo para inferencia aproximada
que produce como resultado lĂmites aproximados inferior y superior de la
funciĂłn de particiĂłn exacta, a base de -empezando a partir de un modelo con
todos los constraints relajados, es decir, con las regiones mĂĄs pequeĂąas posibleincrementalmente
aĂąadir regiones mĂĄs grandes a la aproximaciĂłn. Los mĂŠtodos
de inferencia aproximada que existen actualmente producen lĂmites superiores
ajustados de la funciĂłn de particiĂłn, pero los lĂmites inferiores suelen ser demasiado
imprecisos o incluso triviales.
El objetivo de este proyecto es investigar estrategias de particiĂłn que mejoren
los lĂmites inferiores obtenidos con el algoritmo de mini-bucket, trabajando dentro
del marco de trabajo de mini-bucket incremental.
Empezamos a partir de la idea de que creemos que deberĂa ser beneficioso
razonar conjuntamente con las variables de un modelo que tienen una alta correlaciĂłn,
y desarrollamos una estrategia para la selecciĂłn de regiones basada en
esa idea. Posteriormente, implementamos nuestra estrategia y exploramos formas
de mejorarla, y finalmente medimos los resultados obtenidos usando nuestra
estrategia y los comparamos con varios mĂŠtodos de referencia.
Nuestros resultados indican que nuestra estrategia obtiene lĂmites inferiores
mĂĄs ajustados que nuestros dos mĂŠtodos de referencia. TambiĂŠn consideramos
y descartamos dos posibles hipĂłtesis que podrĂan explicar esta mejora.Els models en graf probabilĂstics, com bĂŠ els camps aleatoris de Markov i les
xarxes bayesianes, ofereixen poderosos marcs de treball per la representaciĂł
del coneixement i el raonament en models amb grans quantitats de variables.
Tanmateix, els problemes dâinferència exacta en models de grafs son NP-hard
en general, el qual ha provocat que es produeixi bastant dâinterès en mètodes
dâinferència aproximats.
El mini-bucket incremental es un marc de treball per a lâinferència aproximada
que produeix com a resultat lĂmits aproximats inferior i superior de la
funciĂł de particiĂł exacta que funciona començant a partir dâun model al qual
se li han relaxat tots els constraints -ĂŠs a dir, un model amb les regions mĂŠs
petites possibles- i anar afegint a lâaproximaciĂł regions incrementalment mĂŠs
grans. Els mètodes dâinferència aproximada que existeixen actualment produeixen
lĂmits superiors ajustats de la funciĂł de particiĂł. Tanmateix, els lĂmits
inferiors acostumen a ser massa imprecisos o fins aviat trivials.
El objectiu dâaquest projecte es recercar estratègies de particiĂł que millorin
els lĂmits inferiors obtinguts amb lâalgorisme de mini-bucket, treballant dins del
marc de treball del mini-bucket incremental.
La nostra idea de partida pel projecte es que creiem que hauria de ser beneficiĂłs
per la qualitat de lâaproximaciĂł raonar conjuntament amb les variables del
model que tenen una alta correlació entre elles, i desenvolupem una estratègia
per a la selecciĂł de regions basada en aquesta idea. Posteriorment, implementem
la nostra estratègia i explorem formes de millorar-la, i finalment mesurem els
resultats obtinguts amb la nostra estratègia i els comparem a diversos mètodes
de referència.
Els nostres resultats indiquen que la nostra estratègia obtĂŠ lĂmits inferiors
mÊs ajustats que els nostres dos mètodes de referència. TambÊ considerem i
descartem dues possibles hipòtesis que podrien explicar aquesta millora.Probabilistic graphical models such as Markov random fields and Bayesian networks
provide powerful frameworks for knowledge representation and reasoning
over models with large numbers of variables. Unfortunately, exact inference
problems on graphical models are generally NP-hard, which has led to signifi-
cant interest in approximate inference algorithms.
Incremental mini-bucket is a framework for approximate inference that provides
upper and lower bounds on the exact partition function by, starting from
a model with completely relaxed constraints, i.e. with the smallest possible
regions, incrementally adding larger regions to the approximation. Current
approximate inference algorithms provide tight upper bounds on the exact partition
function but loose or trivial lower bounds.
This project focuses on researching partitioning strategies that improve the
lower bounds obtained with mini-bucket elimination, working within the framework
of incremental mini-bucket.
We start from the idea that variables that are highly correlated should be
reasoned about together, and we develop a strategy for region selection based
on that idea. We implement the strategy and explore ways to improve it, and
finally we measure the results obtained using the strategy and compare them to
several baselines.
We find that our strategy performs better than both of our baselines. We
also rule out several possible explanations for the improvement
Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty
There is a growing need for methods which can capture uncertainties and
answer queries over graph-structured data. Two common types of uncertainty are
uncertainty over the attribute values of nodes and uncertainty over the
existence of edges. In this paper, we combine those with identity uncertainty.
Identity uncertainty represents uncertainty over the mapping from objects
mentioned in the data, or references, to the underlying real-world entities. We
propose the notion of a probabilistic entity graph (PEG), a probabilistic graph
model that defines a distribution over possible graphs at the entity level. The
model takes into account node attribute uncertainty, edge existence
uncertainty, and identity uncertainty, and thus enables us to systematically
reason about all three types of uncertainties in a uniform manner. We introduce
a general framework for constructing a PEG given uncertain data at the
reference level and develop highly efficient algorithms to answer subgraph
pattern matching queries in this setting. Our algorithms are based on two novel
ideas: context-aware path indexing and reduction by join-candidates, which
drastically reduce the query search space. A comprehensive experimental
evaluation shows that our approach outperforms baseline implementations by
orders of magnitude
Classification of tight contact structures on small Seifert fibered L-spaces
The Ozsvath-Szabo contact invariant is a complete classification invariant
for tight contact structures on small Seifert fibered 3-manifolds which are
L-spaces.Comment: 30 pages, 3 figure
The Impact of Petri Nets on System-of-Systems Engineering
The successful engineering of a large-scale system-of-systems project towards deterministic behaviour depends on integrating autonomous components using international communications standards in accordance with dynamic requirements. To-date, their engineering has been unsuccessful: no combination of top-down and bottom-up engineering perspectives is adopted, and information exchange protocol and interfaces between components are not being precisely specified. Various approaches such as modelling, and architecture frameworks make positive contributions to system-of-systems specification but their successful implementation is still a problem.
One of the most popular modelling notations available for specifying systems, UML, is intuitive and graphical but also ambiguous and imprecise. Supplying a range of diagrams to represent a system under development, UML lacks simulation and exhaustive verification capability. This shortfall in UML has received little attention in the context of system-of-systems and there are two major research issues:
1. Where the dynamic, behavioural diagrams of UML can and cannot be used to model and analyse system-of-systems
2. Determining how Petri nets can be used to improve the specification and analysis of the dynamic model of a system-of-systems specified using UML
This thesis presents the strengths and weaknesses of Petri nets in relation to the specification of system-of-systems and shows how Petri net models can be used instead of conventional UML Activity Diagrams. The model of the system-of-systems can then be analysed and verified using Petri net theory. The Petri net formalism of behaviour is demonstrated using two case studies from the military domain. The first case study uses Petri nets to specify and analyse a close air support mission. This case study concludes by indicating the strengths, weaknesses, and shortfalls of the proposed formalism in system-of-systems specification. The second case study considers specification of a military exchange network parameters problem and the results are compared with the strengths and weaknesses identified in the first case study.
Finally, the results of the research are formulated in the form of a Petri net enhancement to UML (mapping existing activity diagram elements to Petri net elements) to meet the needs of system-of-systems specification, verification and validation
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