<rdf:RDF
 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
 xmlns="http://purl.org/rss/1.0/"
 xmlns:dc="http://purl.org/dc/elements/1.1/"
 xmlns:cc="http://web.resource.org/cc/"
 >
<!--
	This ontology document is licensed under the Creative Commons
	Attribution License. To view a copy of this license, visit
	http://creativecommons.org/licenses/by/2.0/ or send a letter to
	Creative Commons, 559 Nathan Abbott Way, Stanford, California
	94305, USA.
-->
 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=probability+distribution">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=probability+distribution]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for probability distribution]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/264/An-Efficient-Method-for-Probabilistic-Knowledge-Integration"/>
      <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/getnews/html/id/32/Zhongli-Ding-defends-dissertation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1066/CDFMR-A-Distributed-Statistical-Analysis-of-Stock-Market-Data-using-MapReduce-with-Cumulative-Distribution-Function"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1019/Quantum-Assisted-Greedy-Algorithms"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/876/Quantum-Assisted-Greedy-Algorithms"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/925/Energy-theft-detection-for-AMI-using-principal-component-analysis-based-reconstructed-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/799/Entropy-Based-Electricity-Theft-Detection-in-AMI-Network"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/798/Joint-transformation-based-detection-of-false-data-injection-attacks-in-smart-grid"/>
      <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/936/Bayesian-Network-Revision-with-Probabilistic-Constraints"/>
      <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/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift"/>
    </rdf:Seq>
  </items>
 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild.  But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy?  For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream.  The problem of responding to such changes when they are known to have occurred has been studied extensively.  In...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/264/An-Efficient-Method-for-Probabilistic-Knowledge-Integration">
  <title><![CDATA[An Efficient Method for Probabilistic Knowledge Integration]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/264/An-Efficient-Method-for-Probabilistic-Knowledge-Integration</link>
  <description><![CDATA[Probabilistic information can come from many different sources and tends to 
involve a  part  of the domain. How can we integrate the different information about probabilities, especially when they may be inconsistent?

   There are several methods dealing with this problem, such as the well
known iterative proportional fitting procedure (IPFP),
proposed by R. Kruithof in 1937 for situations that are consistent,  and the GEMA algorithm (Generalized Expectation Maximization Algorithm) giv...]]></description>
  <dc:date>2008-10-14</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/getnews/html/id/32/Zhongli-Ding-defends-dissertation">
  <title><![CDATA[Zhongli Ding defends dissertation]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/32/Zhongli-Ding-defends-dissertation</link>
  <description><![CDATA[Zhongli Ding successfully defended her Ph.D. dissertation
entitled "BayesOWL: A Probabilistic Framework for Uncertainty in
Semantic Web" on December 5, 2005.  Dr. Ding came to UMBC in the Fall
of 1999 after receiving her undergraduate degree from the University
of Science and Technology of China in Hefei.  She joined the ebquity
lab in 2000 and has worked closely with Professor Yun Peng, who was
her mentor and dissertation supervisor.  She received a Masters degree in
Computer Scie...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1066/CDFMR-A-Distributed-Statistical-Analysis-of-Stock-Market-Data-using-MapReduce-with-Cumulative-Distribution-Function">
  <title><![CDATA[CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1066/CDFMR-A-Distributed-Statistical-Analysis-of-Stock-Market-Data-using-MapReduce-with-Cumulative-Distribution-Function</link>
  <description><![CDATA[The stock market generates massive data daily on
top of a deluge of historical data. Investors and traders look to
stock market data analysis for assurance in their investments, a
prime indicator of our global economy. This has led to immense
popularity in the topic, and consequently, much research has been
done on stock market predictions and future trends. However,
due to the relatively slow electronic trading systems and order
processing times, the velocity of data, the variety of d...]]></description>
  <dc:date>2023-07-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1019/Quantum-Assisted-Greedy-Algorithms">
  <title><![CDATA[Quantum-Assisted Greedy Algorithms]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1019/Quantum-Assisted-Greedy-Algorithms</link>
  <description><![CDATA[We show how to leverage quantum annealers (QAs) to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making locally optimal choices at each stage, we use QAs that sample from the ground state of problem-dependent Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables. More specifically, we look at each spin of the Ising model as a random variab...]]></description>
  <dc:date>2022-07-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/876/Quantum-Assisted-Greedy-Algorithms">
  <title><![CDATA[Quantum-Assisted Greedy Algorithms]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/876/Quantum-Assisted-Greedy-Algorithms</link>
  <description><![CDATA[We show how to leverage quantum annealers to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making locally optimal choices at each stage, we use quantum annealers that sample from the ground state of Ising Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables. More specifically, we look at each spin in the Ising model as a random variable a...]]></description>
  <dc:date>2019-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/925/Energy-theft-detection-for-AMI-using-principal-component-analysis-based-reconstructed-data">
  <title><![CDATA[Energy theft detection for AMI using principal component analysis based reconstructed data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/925/Energy-theft-detection-for-AMI-using-principal-component-analysis-based-reconstructed-data</link>
  <description><![CDATA[To detect energy theft attacks in advanced metering infrastructure (AMI), we propose a detection method based on principal component analysis (PCA) approximation. PCA approximation is introduced by dimensionality reduction of high dimensional AMI data and the authors extract the underlying consumption trends of a consumer that repeat on a daily or weekly basis. AMI data is reconstructed using principal components and used for computing relative entropy. In the proposed method, relative entrop...]]></description>
  <dc:date>2019-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/799/Entropy-Based-Electricity-Theft-Detection-in-AMI-Network">
  <title><![CDATA[Entropy Based Electricity Theft Detection in AMI Network]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/799/Entropy-Based-Electricity-Theft-Detection-in-AMI-Network</link>
  <description><![CDATA[Advanced metering infrastructure (AMI), one of the prime components of the smart grid, has many benefits like demand response and load management. Electricity theft, a key concern in AMI security since smart meters used in AMI are vulnerable to cyber attacks, causes millions of dollar in financial losses to utilities every year. In light of this problem, the authors propose an entropy-based electricity theft detection scheme to detect electricity theft by tracking the dynamics of consumption ...]]></description>
  <dc:date>2017-10-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/798/Joint-transformation-based-detection-of-false-data-injection-attacks-in-smart-grid">
  <title><![CDATA[Joint transformation based detection of false data injection attacks in smart grid]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/798/Joint-transformation-based-detection-of-false-data-injection-attacks-in-smart-grid</link>
  <description><![CDATA[For reliable operation and control of smart grid, estimating the correct states is of utmost importance to the system operator. With recent incorporation of information technology and Advanced Metering Infrastructure (AMI), the futuristic grid is more prone to cyber-threats. The False Data Injection (FDI) attack is one of the most thoroughly researched cyber-attacks. Intelligently crafted, it can cause false estimation of states, which further seriously affects the entire power system operati...]]></description>
  <dc:date>2017-06-28</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/936/Bayesian-Network-Revision-with-Probabilistic-Constraints">
  <title><![CDATA[Bayesian Network Revision with Probabilistic Constraints]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/936/Bayesian-Network-Revision-with-Probabilistic-Constraints</link>
  <description><![CDATA[This paper deals with an important probabilistic knowledge integration problem: revising a Bayesian network (BN) to satisfy a set of probability constraints representing new or more specific knowledge. We propose to solve this problem by adopting IPFP (iterative proportional fitting procedure) to BN. The resulting algorithm E-IPFP integrates the constraints by only changing the conditional probability tables (CPT) of the given BN while preserving the network structure; and the probability dis...]]></description>
  <dc:date>2012-03-21</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/resource/html/id/162/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/resource/html/id/162/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/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/306/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild. But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy? For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream. The problem of responding to such changes when they are known to have occurred has been studied extensively. In thi...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
</rdf:RDF>
