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 <channel rdf:about="http://ebiquity.umbc.edu//tag/html/bayesian reasoning/?t=bayesian+reasoning">
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  <link><![CDATA[http://ebiquity.umbc.edu//tag/html/bayesian reasoning/?t=bayesian+reasoning]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for bayesian reasoning]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/386/Prediction-markets-for-fun-feedback-and-the-future"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/165/Semantically-Linked-Bayesian-Networks-A-Framework-for-Probabilistic-Inference-Over-Multiple-Bayesian-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/129/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/929/Modify-Bayesian-Network-Structure-with-Inconsistent-Constraints"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/931/Inconsistent-Knowledge-Integration-with-Bayesian-Network"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/489/A-Practical-Tool-for-Uncertainty-in-OWL-Ontologies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/378/Belief-Update-in-Bayesian-Networks-Using-Uncertain-Evidence"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/380/Semantic-Linked-Bayesian-Networks-A-Framework-for-Bayesian-Network-Mapping"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/278/BayesOWL-A-Probabilistic-Framework-for-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/249/A-Bayesian-Network-Approach-to-Ontology-Mapping"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/271/BayesOWL-Uncertainty-Modeling-in-Semantic-Web-Ontologies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/256/BayesOWL-binary-file"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/257/BayesOWL-source-file"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/386/Prediction-markets-for-fun-feedback-and-the-future">
  <title><![CDATA[Prediction markets for fun, feedback and the future]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/386/Prediction-markets-for-fun-feedback-and-the-future</link>
  <description><![CDATA[Prediction markets, when they work well, solve a fundamental problem: how to aggregate individual beliefs into a meaningful quantitative estimate of the probability that a given event will occur. They also provide incentives for people to disseminate privately-held information. I will describe one way to help these markets work better: incorporating a learning agent who provides liquidity, called a market maker. Along the way, the design of this agent raises and solves some fundamental proble...]]></description>
  <dc:date>2011-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/165/Semantically-Linked-Bayesian-Networks-A-Framework-for-Probabilistic-Inference-Over-Multiple-Bayesian-Networks">
  <title><![CDATA[Semantically-Linked Bayesian Networks: A Framework for Probabilistic Inference Over Multiple Bayesian Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/165/Semantically-Linked-Bayesian-Networks-A-Framework-for-Probabilistic-Inference-Over-Multiple-Bayesian-Networks</link>
  <description><![CDATA[At the present time, Bayesian networks (BNs), presumably the most popular uncertainty inference framework, are still widely used as standalone systems. When the problem itself is distributed, domain knowledge has to be centralized and unified before a single BN can be created. Alternatively, separate BNs describing related sub-domains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving even if the interdependent relations between var...]]></description>
  <dc:date>2006-08-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/129/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web">
  <title><![CDATA[BayesOWL: A Probabilistic Framework  for Uncertainty in Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/129/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web</link>
  <description><![CDATA[Ph.D. Dissertation Defense
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed a theoretical framework, named BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model for probabilistic interdependency, into the web ontology language OWL. This framework consists of three key components:

 a representation for encoding the probability distributions as OWL classes;
 a...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies">
  <title><![CDATA[UMBC OntoMapper: A Tool For Mapping Between Two Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies</link>
  <description><![CDATA[Forcing all communicating agents to share a common ontology is infeasible. A group of
people with similar interests usually has its own organizational schemes for documents. This
organization may be in the form of an ontology. Different agents may define very different
ontologies, and the semantics for the same terms may be very different in their ontologies. A
mapping from one agent's ontology to another agent's ontology is required to facilitate
communication between agents.
   Thi...]]></description>
  <dc:date>2001-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/929/Modify-Bayesian-Network-Structure-with-Inconsistent-Constraints">
  <title><![CDATA[Modify Bayesian Network Structure with Inconsistent Constraints]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/929/Modify-Bayesian-Network-Structure-with-Inconsistent-Constraints</link>
  <description><![CDATA[This paper presents a theoretical framework and related methods for integrating probabilistic knowledge represented as low dimensional distributions (also called constraints) into an existing Bayesian network (BN), even when these constraints are inconsistent with the structure of the BN due to dependencies among relevant variables in the constraints being absent in the BN. Within this framework, a method has been developed to identify structural inconsistencies. Methods have also been develo...]]></description>
  <dc:date>2016-09-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/931/Inconsistent-Knowledge-Integration-with-Bayesian-Network">
  <title><![CDATA[Inconsistent Knowledge Integration with Bayesian Network]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/931/Inconsistent-Knowledge-Integration-with-Bayesian-Network</link>
  <description><![CDATA[Given a Bayesian network (BN) representing a probabilistic knowledge base of a domain, and a set of low-dimensional probability distributions (also called constraints) representing pieces of new knowledge coming from more up-to-date or more specific observations for a certain perspective of the domain, we present a theoretical framework and related methods for integrating the constraints into the BN, even when these constraints are inconsistent with the structure of the BN due to dependencies...]]></description>
  <dc:date>2016-05-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/489/A-Practical-Tool-for-Uncertainty-in-OWL-Ontologies">
  <title><![CDATA[A Practical Tool for Uncertainty in OWL Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/489/A-Practical-Tool-for-Uncertainty-in-OWL-Ontologies</link>
  <description><![CDATA[Previously we have proposed a theoretical framework, named BayesOWL, which translates an OWL taxonomy of concept classes into a Bayesian network (BN) and incorporates consistent probabilistic information about the concept classes into the translated BN. In this paper, we extend the original framework to support general OWL DL ontologies and to effectively deal with inconsistent probabilistic information. We have also implemented the BayesOWL prototype system, which can be used as a practical ...]]></description>
  <dc:date>2010-02-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration">
  <title><![CDATA[An Efficient Method for Probabilistic Knowledge Integration]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration</link>
  <description><![CDATA[This paper presents an efficient method, SMOOTH, for modifying a joint probability distribution to satisfy a set of inconsistent constraints. It extends the well-known “iterative proportional fitting procedure” (IPFP), which only works with consistent constraints. Comparing with existing methods, SMOOTH is computationally more efficient and insensitive to data. Moreover, SMOOTH can be easily integrated with Bayesian networks for Bayes reasoning with inconsistent constraints.]]></description>
  <dc:date>2008-11-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/378/Belief-Update-in-Bayesian-Networks-Using-Uncertain-Evidence">
  <title><![CDATA[Belief Update in Bayesian Networks Using Uncertain Evidence]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/378/Belief-Update-in-Bayesian-Networks-Using-Uncertain-Evidence</link>
  <description><![CDATA[This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffrey’s rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods...]]></description>
  <dc:date>2006-11-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/380/Semantic-Linked-Bayesian-Networks-A-Framework-for-Bayesian-Network-Mapping">
  <title><![CDATA[Semantic-Linked Bayesian Networks: A Framework for Bayesian Network Mapping]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/380/Semantic-Linked-Bayesian-Networks-A-Framework-for-Bayesian-Network-Mapping</link>
  <description><![CDATA[At the present time, Bayesian networks (BNs), presumably the
most popular uncertainty inference framework, are still
widely used as standalone systems. When the problem itself
is distributed, domain knowledge has to be centralized and
unified before a single BN can be created. Alternatively,
separate BNs describing related sub-domains or different
aspects of the same domain may be created, but it is
difficult to combine them for problem solving even if the
interdependent relations bet...]]></description>
  <dc:date>2006-08-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/278/BayesOWL-A-Probabilistic-Framework-for-Semantic-Web">
  <title><![CDATA[BayesOWL: A Probabilistic Framework for Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/278/BayesOWL-A-Probabilistic-Framework-for-Semantic-Web</link>
  <description><![CDATA[To address the difficult but important problem of modeling uncertainty in semantic web,
this research takes a probabilistic approach and develops a theoretical framework, named
BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model
for probabilistic interdependency, into the web ontology language OWL. This framework
consists of three key components: 1) a representation of probabilistic constraints as OWL
statements; 2) a set of structural translation rules and...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/249/A-Bayesian-Network-Approach-to-Ontology-Mapping">
  <title><![CDATA[A Bayesian Network Approach to Ontology Mapping]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/249/A-Bayesian-Network-Approach-to-Ontology-Mapping</link>
  <description><![CDATA[This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BN. Probabilities needed for constructing conditional probability tables (CPT...]]></description>
  <dc:date>2005-11-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/271/BayesOWL-Uncertainty-Modeling-in-Semantic-Web-Ontologies">
  <title><![CDATA[BayesOWL: Uncertainty Modeling in Semantic Web Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/271/BayesOWL-Uncertainty-Modeling-in-Semantic-Web-Ontologies</link>
  <description><![CDATA[It is always essential but di±cult to capture incomplete, partial or uncertain
knowledge when using ontologies to conceptualize an application domain or to
achieve semantic interoperability among heterogeneous systems. This chapter
presents an on-going research on developing a framework which augments and
supplements the semantic web ontology language OWL for representing and
reasoning with uncertainty based on Bayesian networks (BN), and its
application in ontology mapping.]]></description>
  <dc:date>2005-10-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints">
  <title><![CDATA[Modifying Bayesian Networks by Probability Constraints]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints</link>
  <description><![CDATA[This paper deals with the following problem:
modify a Bayesian network to satisfy a given set
of probability constraints by only changeing its
conditional probability tables while keeping the probability
distribution of the resulting network  as
close as possible to that of the original.
We solve this problem by extending
IPFP (iterative proportional fitting procedure) to
probability distributions represented by Bayesian
networks. The resulting algorithm, E-IPFP is further
developed...]]></description>
  <dc:date>2005-07-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/256/BayesOWL-binary-file">
  <title><![CDATA[BayesOWL binary file]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/256/BayesOWL-binary-file</link>
  <description><![CDATA[BayesOWL is Java-based tool. It can be used to extract taxonomies from OWL ontologies, translate taxonomies into Bayesian Networks and integrate uncertainty knowledge into BNs.]]></description>
  <dc:date>2008-12-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/257/BayesOWL-source-file">
  <title><![CDATA[BayesOWL source file]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/257/BayesOWL-source-file</link>
  <description><![CDATA[BayesOWL is Java-based tool. It can be used to extract taxonomies from OWL ontologies, translate taxonomies into Bayesian Networks and integrate uncertainty knowledge into BNs.]]></description>
  <dc:date>2008-12-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-">
  <title><![CDATA[BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web (pdf)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-</link>
  <description><![CDATA[Ph.D. Dissertation Defense
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed a theoretical framework, named BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model for probabilistic interdependency, into the web ontology language OWL. This framework consists of three key components:

 a representation for encoding the probability distributions as OWL classes;
 a...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks">
  <title><![CDATA[Semantically-Linked Bayesian Networks]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks</link>
  <description><![CDATA[At the present time, Bayesian networks (BNs), presumably the most popular uncertainty inference framework, are still widely used as standalone systems. When the problem itself is distributed, domain knowledge has to be centralized and unified before a single BN can be created. Alternatively, separate BNs describing related sub-domains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving even if the interdependent relations between var...]]></description>
  <dc:date>2006-08-02</dc:date>
 </item>
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