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	Bayesian network - Wikipedia, the free encyclopedia
		Thus, a Bayesian network represents a set of variables together with a joint probability distribution with explicit independence assumptions.
		A causal Bayesian network is a Bayesian network where the directed arcs of the graph are interpreted as representing causal relations in some real domain.
		Bayesian networks are used for modelling knowledge in gene regulatory networks, medicine, engineering, text analysis, image processing, data fusion, and decision support systems.
	en.wikipedia.org /wiki/Bayesian_network (1401 words)

	An Introduction to Bayesian Networks and their Contemporary Applications
		Bayesian Networks are becoming an increasingly important area for research and application in the entire field of Artificial Intelligence.
		Bayesian networks are useful for both inferential exploration of previously undetermined relationships among variables as well as descriptions of these relationships upon discovery.
		Bayesian inference is useful because it allows the inference system to construct its own potential systems of meaning upon the data.
	www.niedermayer.ca /papers/bayesian/bayes.html (3803 words)

	A Tutorial on Learning With Bayesian Networks
		A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest.
		Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention.
		Four, Bayesian statistical methods in conjunction with bayesian networks offer an efficient and principled approach for avoiding the overfitting of data.
	research.microsoft.com /research/pubs/view.aspx?msr_tr_id=MSR-TR-95-06 (206 words)

	Intel to release machine learning libraries - 02 May 2003 - New Scientist
		Bayesian networks combine two mathematical approaches, Bayesian statistics and graph theory, to provide a powerful means of modelling probabilities based on continuously updated information.
		Bayesian networks are already used in some programs which, for example, can learn the characteristics of unwanted "spam" email by analysis of previous messages that the user has classified as legitimate or not.
		The new Bayesian network libraries will be optimised for Intel's microprocessor hardware, which is the most commonly used worldwide.
	www.newscientist.com /article.ns?id=dn3691 (438 words)

	What is Bayesian Learning? (Site not responding. Last check: 2007-09-08)
		The Bayesian school of statistics is based on a different view of what it means to learn from data, in which probability is used to represent uncertainty about the relationship being learned (a use that is shunned in conventional--i.e., frequentist--statistics).
		Network weights that seemed plausible before, but which don't match the data very well, will now be seen as being much less likely, while the probability for values of the weights that do fit the data well will have increased.
		Bayesian learning should not be confused with the "Bayes classifier." In the latter, the distribution of the inputs given the target class is assumed to be known exactly, and the prior probabilities of the classes are assumed known, so that the posterior probabilities can be computed by a (theoretically) simple application of Bayes' theorem.
	www.faqs.org /faqs/ai-faq/neural-nets/part3/section-7.html (1872 words)

	BNGenerator (Site not responding. Last check: 2007-09-08)
		Bayesian networks are popular representations for uncertainty; the idea is to use a directed acyclic graph and a collection of conditional probability distributions to represent a possibly complex joint probability distribution.
		To generate a Bayesian network, we need to generate a directed acyclic graph with N nodes, and for each node we must generate a number V of values, and then we must generate a probability distribution p(XP).
		It has been observed that in practice Bayesian networks are rather sparse graphs, in the sense that nodes have a few parents and children (that is, the degree of the nodes is not too large).
	www.pmr.poli.usp.br /ltd/Software/BNGenerator (2611 words)

	A Plan for Spam
		I thought I was being very clever, but I found that the Bayesian filter did the same thing for me, and moreover discovered of a lot of words I hadn't thought of.
		To beat Bayesian filters, it would not be enough for spammers to make their emails unique or to stop using individual naughty words.
		You could use a Bayesian filter to rate the site just as you would an email, and whatever was found on the site could be included in calculating the probability of the email being a spam.
	www.paulgraham.com /spam.html (5051 words)

	Bayesian Belief Networks - a CompInfo Directory
		Belief networks are particularly useful for diagnostic applications and have been used in many deployed systems.
		Bayesian Belief Networks (BBNs) are at the cutting edge of expert systems research and development.
		It is primarily for information on belief networks (BNs) which are also known as graphical models, Bayesian networks, Bayesian Belief Networks (BBNs), Markov networks, Chain graphs and Causal Probabilistic Networks (some of these are names for variants).
	www.compinfo-center.com /ai/bayesian_belief_networks.htm (682 words)

	BTS \| A Bayesian Network Model of Two-Car Accidents (Site not responding. Last check: 2007-09-08)
		An advantage of the Bayesian network method presented here is its complex approach where system variables are interdependent and where no dependent and independent variables are needed.
		These networks are an important tool in the design and analysis of machine learning algorithms and are based on the idea of modularity whereby a complex system is built by combining simpler parts.
		Bayesian networks can streamline the process, because they are a compact way of factoring the joint probability distribution into local, conditional distributions that reduce the number of multiplications necessary to obtain the probability of specific events.
	www.bts.gov /publications/journal_of_transportation_and_statistics/volume_07_number_23/html/paper_02 (4956 words)

	LOCALLY DEFINED QUASI-BAYESIAN NETWORKS (Site not responding. Last check: 2007-09-08)
		In a standard Bayesian network, irrelevance and independence constraints are implicit in Expression (1); this expression guarantees that a variable is independent of all its non-descendants given its parents [
		It seems more appropriate to ask a decision maker to explicitly indicate which qualitative constraints are to be enforced in a Quasi-Bayesian network, and to ask for irrelevance constraints instead of independence constraints, because irrelevance and independence are not equivalent in Quasi-Bayesian models (Section 2.2).
		The key fact is that a directed acyclic graph and a collection of local credal sets may admit more than one extension; the next sections investigate two important types of extension.
	www.cs.cmu.edu /~qbayes/QuasiBayesianNetworks/UAI98/HTML/node9.html (211 words)

	CiteULike: Tag bayesian (Site not responding. Last check: 2007-09-08)
		Bayesian network analysis of signaling networks: a primer.
		Derivation and validation of a Bayesian network to predict pretest probability of venous thromboembolism.
		Bayesian machine learning and its potential applications to the genomic study of oral oncology.
	www.citeulike.org /tag/bayesian (808 words)

	Untitled Document (Site not responding. Last check: 2007-09-08)
		The Bayesian approach has come full-circle towards better modeling in the form of Bayesian Networks and Influence Diagrams (for a detailed treatise, see Alice Agogino's paper, Management of Uncertainty with Influence Diagrams).
		Noetic Systems has developed a companion too to their belief network tool Ergo, called Cogito, the result of an effort to automatically build a bayesian network form a database of clinical data using pre-defined algorithms.
		This network is described in Beinlich, Ingo, H. Suermondt, R. Chavez, and G. Cooper (1989) "The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks" in Proc.
	www.medinformatics.org /mdi207/lab3.html (4190 words)

	Graphical Models
		The simplest conditional independence relationship encoded in a Bayesian network can be stated as follows: a node is independent of its ancestors given its parents, where the ancestor/parent relationship is with respect to some fixed topological ordering of the nodes.
		For example, consider the water sprinkler network, and suppose we observe the fact that the grass is wet.
		Note that "temporal Bayesian network" would be a better name than "dynamic Bayesian network", since it is assumed that the model structure does not change, but the term DBN has become entrenched.
	www.cs.berkeley.edu /~murphyk/Bayes/bayes.html (6628 words)

	Bayesian Network Projects
		This is a brief description of applied research with Bayesian Networks undertaken at the School of Computer Science.
		A number of possible improvements have been identified: combining multiple networks into a single network that includes site and type "spatial" information; explicitly including temporal information for predicting over different time frames; and identification of suitable evaluation measures and applying them on the existing and subsequent models.
		Bayesian nets are applied to better predict which web pages the user will attempt to retrieve next, which can be used to improve efficiency in handling web traffic.
	www.csse.monash.edu.au /bai/projects.html (1486 words)

	Dr. Rina Dechter @ UCI
		One of the main challenges in building intelligent systems is the ability to reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian belief networks.
		Intelligent systems based on Bayesian networks are currently being used in a number of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, and data mining.
		The objective of this class is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of belief networks.
	www.ics.uci.edu /~dechter/ics-275b/fall-00 (507 words)

	Bayesline - A Bayesian Network Framework
		Bayesline is Bayesian Network Framework (a very good tutorial on Bayesian networks can be found here), allowing users and developers to create Bayesian networks as well as specialized types knowledge to be assigned to the nodes of the network.
		Variables are the nodes in Bayesian Networks, clusters are sets of those variables and valuations are representing (probabilistic) knowledge about variables and clusters.
		A Bayesian Network in this sense is the structure that contains all variables, clusters and valuations.
	bayesline.sourceforge.net (656 words)

	Induction of Selective Bayesian Network Classifiers - Singh, Provan, Langley (ResearchIndex)
		The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy.
		We compare the behavior of this selective Bayesian network classifier with that of (a) Bayesian network classifiers that incorporate all attributes, (b) selective and non-selective naive Bayesian classifiers, and...
		17 A causal probabilistic network for interpretation of electro..
	citeseer.ist.psu.edu /singh96induction.html (832 words)

	Dr. Rina Dechter @ UCI
		One of the main challenges in building intelligent systems is the ability to reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian networks, also called graphical models.
		Intelligent systems based on Bayesian networks are being used in a variety of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, bioinformatics and data mining.
		The objective of this class is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of Bayesian networks.
	www.ics.uci.edu /~dechter/ics-275b/spring-05 (445 words)

	Bayesian Belief Networks (Site not responding. Last check: 2007-09-08)
		The Bayesian Network uses the concept of Conditional Independence to simplify our calculations, allowing us to derive the information which we need from the joint distribution using a much smaller number of conditional probabilities.
		The effects of new information are propagated through the network using Bayes's Theorem, changing the belief values of other diseases and signs.
		Hence, the graph is a Bayesian Belief Network.
	vie.dis.strath.ac.uk /vie/CaDDiS/docs/Bayesian_Belief_Networks.html (475 words)

	Bayesian network
		A Bayesian Network is an acyclic directed weighted graph.
		Bayesian Networks are also known under the following names: bayesian belief networks, belief networks, causal probabilistic networks or knowledge maps.
		Bayesian Networks have been applied to heuristic search, medical diagnosis, map learning, language understanding and military decision.
	osiris.tuwien.ac.at /~wgarn/bbn/bbn.html (217 words)

	The Interchange Format for Bayesian Networks
		Bayesian Network Interchange Format proposal, referred to as BNIF.
		car-starts.bif, a somewhat large network contributed by Sreekanth Nagarajan, based on the automobile belief network that David Heckerman and Jack Breese presented in the March, 1995 issue of Communications of the ACM.
		This system gives the user a graphical interface for construction of Bayesian networks and performs inferences through a server connection (the inference engine is maintained at Oregon State University).
	www-2.cs.cmu.edu /~fgcozman/Research/InterchangeFormat/Old/xmlbif02.html (2684 words)

	Belief Nets (aka Bayesian Nets)
		An Introduction to Bayesian Networks and their Contemporary Applications (1998)
		Jensen, An Introduction to Bayesian Networks, Springer Verlag, 1996
		Bayesian Networks without Tears, AI Magzaine, 12(4), Winter 91, 50-63
	www.cs.ualberta.ca /~greiner/bn.html (201 words)

	First Model Temporal Bayesian Network (FMTBN)
		In a Bayesian network, the joint conditional probability of a complete assignment is found using the chain rule [2, pg.
		The remainder of the network is a Bayesian network and the ordering is constructed accordingly.
		The conciseness, in terms of probabilities needed, of the TRV notation, suggests that using Bayesian networks for computation may not be the best approach.
	www.cs.indiana.edu /l/www/event/maics96/Proceedings/Young/node3.html (792 words)

	EBayes: Embedded Bayesian Networks
		A whole Bayesian network system can be built using these classes if needed; the EBayes.class program is a simple demonstration of what can be achieved with the EBayes engine.
		Bayesian networks have been used as a fundamental tool for the representation and manipulation of beliefs in Artificial Intelligence.
		An illustrative example of an embedded Bayesian network would be the self-diagnostic ability of a smart refrigerator.
	www-2.cs.cmu.edu /~javabayes/EBayes/Doc (1245 words)

	A Bayesian network based framework for multi-criteria decision making
		In this framework, a decision problem is represented by an ID where each decision node represents the set of alternatives for a decision, a utility node represents the set of objectives (decision maker’s preferences), decision criteria and internal or external factors that may affect the criteria are represented by chance nodes.
		The joint probability distribution, which is compactly captured by the network structure and CPT, encodes the domain expert’s knowledge of interdependency between variables.
		The decision problem is then treated as an optimization problem: recommend the decision alternative which optimizes the expected utility, given observations of some external factors and preferences made by the decision maker.
	ebiquity.umbc.edu /paper/html/id/281/A-Bayesian-network-based-framew... (399 words)

	Bayesian AI
		This is a tutorial which we have developed for presentation of Bayesian AI methods at academic conferences and for industry.
		Bayesian networks and Causal Modelling (powerpoint) Seminar given Feb 4th 2005, Australian Centre for Field Robotics, University of Sydney.
		Using Bayesian networks for Water Quality Prediction in Sydney Harbour (powerpoint) Seminar given Feb 3rd 2005, NSW Department of Environment and Conservation, Sydney.
	www.csse.monash.edu.au /bai (327 words)

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