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Topic: Graphical model

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	Graphical Models
		Probabilistic graphical models are graphs in which nodes represent random variables, and the (lack of) arcs represent conditional independence assumptions.
		In principle, it is straightforward to use graphical models to do Bayesian learning: the parameters, being random variables, become nodes as well, and the goal is the standard inference problem of computing posterior distributions on the (parameter) nodes.
		Classical control theory is mostly concerned with the special case where the graphical model is a Linear Dynamical System and the utility function is negative quadratic loss, e.g., consider a missile tracking an airplane: its goal is to minimize the squared distance between itself and the target.
	www.cs.ubc.ca /~murphyk/Bayes/bayes.html (6628 words)

	Encyclopedia: Graphical model (Site not responding. Last check: 2007-11-04)
		In probability theory and statistics, a graphical model (GM) represents dependencies among random variables by a graph in which each random variable is a node.
		This type of graphical model is known as a directed graphical model, Bayesian network, or belief network.
		A recent application of graphical models is to describe gene regulatory networks.
	www.nationmaster.com /encyclopedia/Graphical-model (587 words)

	Graphical model (Site not responding. Last check: 2007-11-04)
		In the simplest case, the network structure of the model is a directed acyclic graph.
		An Atlas of Consonance The author presents a graphical tool for the visualization of consonance and dissonance, based on an acoustical model of the interaction of musical tones.
		SyncCharts Graphical formalism (name of model, a syncChart is an instance) dedicated to reactive system modeling.
	www.serebella.com /encyclopedia/article-Graphical_model.html (599 words)

	Graphical Models
		Probabilistic graphical models are graphs in which nodes represent random variables, and the (lack of) arcs represent conditional independence assumptions.
		In principle, it is straightforward to use graphical models to do Bayesian learning: the parameters, being random variables, become nodes as well, and the goal is the standard inference problem of computing posterior distributions on the (parameter) nodes.
		Classical control theory is mostly concerned with the special case where the graphical model is a Linear Dynamical System and the utility function is negative quadratic loss, e.g., consider a missile tracking an airplane: its goal is to minimize the squared distance between itself and the target.
	www.ai.mit.edu /~murphyk/Bayes/bnintro.html (6598 words)

	[No title] (Site not responding. Last check: 2007-11-04)
		In particular discrete graphical models, also called Bayesian networks, have experienced a great success, especially in the area of probabilistic expert systems.
		In many applied contexts, a discrete graphical model is a multi-way contingency table whose cell-probabilities obey some functional constraints imposed by the conditional independence structure embodied in the graph.
		Given an arbitrary decomposable discrete graphical model under multinomial sampling, we rewrite the joint distribution of the observations in a natural exponential family form, using a general and powerful notation, based on subsets of cliques and separators in the underlying graph, to index both canonical statistics and parameters.
	cri.haifa.ac.il /events/2004/csstat/csstat04_abstract.php?talk=13 (230 words)

	Graphical Models on Contingency Tables (Site not responding. Last check: 2007-11-04)
		Graphical models are for contingency tables log-linear interaction models that can be represented by a simple undirected graph with as many vertices as the table has dimension.
		Further, all these models can be given an interpretation in terms of conditional independence and the interpretation can be read directly off the graph in the form of a Markov property.
		In Darroch:Lauritzen:Speed:80 graphical models are defined by the close connection between the theory of Markov fields and that of log-linear interaction models for contingency tables.
	www.math.aau.dk /~jhb/Thesis/PartI/node14.html (132 words)

	Augmented Telerobotic Control: a visual interface for unstructured environments (Site not responding. Last check: 2007-11-04)
		If a graphical model of the robot can be used by the operator to manipulate the objects which she sees and interprets in video image, it can serve as a visual display for the task simulation.
		After modelling the picked object, the graphical robot is manipulated to generate a path from the current position to the position in which the edges of modelled wireframe cube gets aligned with the surface.
		The concept of interactive modelling is proposed as a means of enabling the human operator to convey limited amounts of data about the video scene to the computer; to gradually develop and refine a quantitative model of portions of the remote world; but not for deriving an accurate model of the entire remote site.
	vered.rose.utoronto.ca /people/anu_dir/papers/atc/atcDND.html (4146 words)

	Tutorials
		By placing constraints within the graphical models framework, I will be able to compare constraint processing with probabilistic reasoning along the two main reasoning paradigms: search-based and inference-based.
		Graphical models are used and studied in various fields, including artificial intelligence, computational biology, image processing, and error-control coding.
		At the core of applying a graphical model lies a common set of challenging problems, including the computation of marginals, modes and log likelihoods, and learning parameters from data.
	research.microsoft.com /uai2004/Tutorials.htm (653 words)

	British Computer Society (BCS) (Site not responding. Last check: 2007-11-04)
		The description "graphical models" encompasses a wide range of statistical (and at times, causal) models that have witnessed a surge in research over the last twenty years in a variety of disciplines, including statistics, artificial intelligence, and neural networks.
		At their most basic level, graphical models contain a set of nodes, a set of edges between the nodes, and a function that is both associated with the graph and constrained by the edges.
		The reader is thus immediately introduced to the usefulness of considering problems using a relatively arbitrary graphical model (as opposed to one with a particular structure).
	www.dcs.ex.ac.uk /bcs-par/newsletter.htm (1286 words)

	finalcopy of paper (Site not responding. Last check: 2007-11-04)
		Once a model of an object in the remote video scene is created, the computer is able to enhance the outlines and further can support the operator with, for example graphical simulation of planned operations.
		Graphical displays can also be used for presenting complex information about the state of the teleoperator system and environmental constraints.
		The graphical model is drawn in wire-frame when it overlaps the real robot, to prevent obscuring the remote robot view.
	vered.rose.utoronto.ca /people/anu_dir/VTC/vtc.html (3986 words)

	BioMed Central \| Full text \| A graphical model approach to automated classification of protein subcellular location ...
		When this graphical model approach is used on synthetic multi-cell images in which the true class of each cell is known, we observe that the ability to distinguish similar classes is improved without suffering any degradation in ability to distinguish dissimilar classes.
		Graphical models have been extensively applied to problems in the computer vision field, such as image segmentation and object recognition, where the pixels in an image can be segmented or classified into two (foreground and background) or more classes [12].
		Since our goal is to apply graphical models to fields with many cells, we need an efficient method for inferring the most likely class for each cell given the results of the single cell classifier for it and its neighbors.
	www.biomedcentral.com /1471-2105/7/90 (6746 words)

	Graphical model -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-11-04)
		In the simplest case, the network structure of the model is a (Click link for more info and facts about directed acyclic graph) directed acyclic graph (DAG).
		Then the GM represents a factorization of the joint (A measure of how likely it is that some event will occur) probability of all random variables.
		Any two nodes that are not in a descendant/ancestor relationship are (Click link for more info and facts about conditionally independent) conditionally independent given the values of their parents.
	www.absoluteastronomy.com /encyclopedia/g/gr/graphical_model.htm (167 words)

	A graphical model for interval training (Site not responding. Last check: 2007-11-04)
		For example, according to the model, it would not be productive to train at 85% of MAP during work intervals of under 1:30 min:s (the number of repetitions would have to be over 30), or over 6:30 min:s (the number of repetitions would be fewer than 3).
		The model can also be used to determine the change that must be made in the number of work intervals of a certain duration when intensity changes.
		The model does not apply to training sessions that consist of work intervals of under 30 seconds; this is a significant shortcoming, which may be corrected in a subsequent version.
	www.coachr.org /a_graphical_model_for_interval.htm (2636 words)

	Research Project: CAREER: A Graphical-Model Based Software Infrastructure for Speech Recognition Research and Education ... (Site not responding. Last check: 2007-11-04)
		Graphical models are a powerful statistical technique for making probabilistic inferences from data.
		So far, these models have not been extensively applied to the speech recognition problem, but because of their expressive power, they have great potential for advancing the state-of-the-art in the field.
		This software package will enable research on graphical model speech recognition that has not been possible before, and will serve as a teaching tool for classes both in the University of Washington, and elsewhere.
	www.ee.washington.edu /research/projects/proj_career.html (259 words)

	Genome Biology \| Full text \| Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana
		For each edge, the conditional dependence of the corresponding gene pairs was modeled with a latent random variable in a structural equation model as described in [31].
		The performance of the graphical modeling approaches was monitored using the rate of true and false positives in receiver operator characteristics (ROC) curves (see [11] for a short introduction).
		Graphical models are based on a more sophisticated measure of conditional dependence among genes.
	www.genomebiology.com /2004/5/11/R92 (6266 words)

	Graphical Model Theory for Wireless Sensor Networks
		Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation.
		The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning.
		Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.
	repositories.cdlib.org /lbnl/LBNL-53452 (174 words)

	Graphical state machine based programming for a graphical user interface patent invention
		The state diagram model provides a graphical structure for handling events associated with the graphical user interface, such as events generated by inputs provided by a user via a graphical user interface element.
		The state diagram model may include a finite state machine, such as a graphical representation of the finite state machine, or any other type of state machine, such as a non-deterministic state machine.
		The graphical element may be any one of the following: 1) a push button, 2) a slider, 3) a radio button, 4) a check box, 5) a text field, 6) a pop-up menu, 7) a list box, 8) a toggle button, 9) a panel, and 10) a button group.
	www.freshpatents.com /Graphical-state-machine-based-programming-for-a-graphical-user-interface-dt20061019ptan20060235548.php (2098 words)

	Brown CS: Brown CS: Computer Vision and Learning Seminar Series
		He is currently a post-doctoral researcher at the MIT AI Lab, where he works with Leslie Kaelbling and Bill Freeman on applying graphical models and machine learning to problems in computer vision and mobile robotics.
		Graphical models provide a powerful general framework for formulating and solving problems of statistical inference and machine learning.
		In many applications of graphical models, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions.
	www.cs.brown.edu /events/vision-learning (417 words)

	Publication - A Constructive Graphical Model Approach for Knowledge-Based Systems: A Vehicle Monitoring Case Study
		Graphical models have been widely applied to uncertain reasoning in knowledge-based systems.
		For many of the problems tackled, a single graphical model is constructed before individual cases are presented and the model is used to reason about each new case.
		We show that the previously held negative belief on the applicability of graphical models to such problems is unjustified.
	mas.cs.umass.edu /paper/256 (275 words)

	Research Project Description
		Modeling is a progressive process where context and detail levels are nested.
		The alignment between models at different organizational and functional levels can be defined by finding the equivalences between the behavior traces of a system represented at these levels.
		By assigning an operational semantics to a visual modeling language, it is possible to improve the language by eliminating issues such as ambiguities, inconsistencies and to check the completeness of the language.
	lamspeople.epfl.ch /rychkova/PhD/Research.html (1338 words)

	Algorithms for graphical models (Site not responding. Last check: 2007-11-04)
		A graphical model is a statistical model for how variables interact, and can be seen as a generalization of the markov chain to more general structures.
		In AI they are popular for modelling knowledge in expert systems, in data mining applications etc., since they have both a sound mathematical basis, and an expressiveness that allows modelling of complex cause-effect structures under uncertainty.
		This is a bottleneck for the usefulness of these models, and much research has been directed towards finding algorithms that are as efficient as possible.
	www.cs.chalmers.se /Cs/Research/Algorithms/phdintro (789 words)

	CFP: Workshop on Probabilistic Graphical Models for Classification (within ECML/PKDD'03) (Site not responding. Last check: 2007-11-04)
		Probabilistic graphical model paradigm has become a popular tool for encoding, representing and handling uncertain knowledge in expert systems over the last decade.
		Currently, interest is emerging within probabilistic graphical models to use them as a tool to induce supervised-unsupervised classification models.
		The workshop audience is intended for researchers in the area of machine learning and probabilistic graphical models, including practitioners of knowledge discovery in databases and statistical and computational learning theorists.
	www.sc.ehu.es /ccwbayes/ecml-pkdd-03-workshop/call.htm (1102 words)

	Neural Networks and Machine Learning Report
		During this period the close links between neural network models and the concepts of conventional statistical pattern recognition were clarified, leading to a stronger theoretical foundation for neural network algorithms, as well as to more effective practical exploitation.
		The majority of artificial neural network models are based on the propagation of continuous variables from one processing unit to the next.
		A chain graph is a probabilistic graphical model admitting both directed and undirected edges, with (partially) directed cycles forbidden.
	www.newton.cam.ac.uk /reports/9798/nnm.html (2303 words)

	Graphical models and variational approximation (Site not responding. Last check: 2007-11-04)
		Graphical models provide an elegant formalism for probabilistic computation that unifies much of the literature on stochastic modeling.
		For sparse networks (e.g., networks in the form of chains or trees, such as hidden Markov models, Kalman filters, and probabilistic decision trees), graphical model algorithms are exact, efficient and practical.
		For dense networks, however, the exact algorithms are often (hopelessly) inefficient, and this fact has hindered the application of this richer class of models to large-scale problems.
	www.clsp.jhu.edu /ws97/jordan_abstract.html (189 words)

	[No title]
		MODEL ARCHITECTURE We will construct activity classifier using a Gaussian mixture model, represented as a simple graphical model and constructed within the BNT framework.
		THE MODEL The model is a two-class, two component mixture model: Class 1 -- "walking" two 31 dimensional Gaussians (means and covariances) with associated mixing parameters.
		In the second, the parameters of the model are adjusted to fit the data based on the soft assignment of the previous step.
	www.media.mit.edu /wearables/mithril/BNT/mixtureBNT.txt (1524 words)

	buntine94a Abstract (Site not responding. Last check: 2007-11-04)
		These graphical models are extended to model data analysis and empirical learning using the notation of plates.
		Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family.
		The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.
	www.jair.org /abstracts/buntine94a.html (197 words)

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