]> 2009/04/07 OpenCyc Knowledge Base Copyright© 2001-2009 Cycorp, Inc., http://www.cyc.com/, Austin, TX, USA This file contains an OWL representation of information contained in the OpenCyc Knowledge Base. The content of this OWL file is licensed under the Creative Commons Attribution 3.0 license whose text can be found at http://creativecommons.org/licenses/by/3.0/legalcode. The content of this OWL file, including the OpenCyc content it represents, constitutes the "Work" referred to in the Creative Commons license. The terms of this license equally apply to, without limitation, renamings and other logically equivalent reformulations of the content of this OWL file (or portions thereof) in any natural or formal language, as well as to derivations of this content or inclusion of it in other ontologies. Mappings between OpenCyc terms and Wikipedia article names provided by Olena Medelyan and Catherine Legg, University of Waikato, NZ under a Creative Commons Attribution 3.0 license. externalID A unique, language-neutral, variable-sized identifier for a concept that can be used to refer unambiguously to that concept across OWL exports or across Cyc inference engines. label A natural-language representation for a concept that is both human readable and readable by the Cyc inference engine. These terms are not guaranteed to refer to the same concept across time but are guaranteed to be consistent within a particular OWL export. Use 'cycAnnot:externalID' for unambiguously referring to a concept across OWL exports or across Cyc inference engines. bayes net BayesNet The collection of all Bayesian Networks intended for probability reasoning. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a network of nodes in which the nodes are random variables that each typically represent the likelihood of a proposition being true (expressed as a real number between zero and one, where zero means certainly false and one means certainly true). See <a href="http://sw.opencyc.org/concept/Mx4rvtSZipwpEbGdrcN5Y29ycA" class="cyc_term">bayesNetOfMicrotheory</a>. Each <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> interconnects a set of propositions (or symbols associated with propositions) together forming a <a href="http://sw.opencyc.org/concept/Mx4rvtXKWZwpEbGdrcN5Y29ycA" class="cyc_term">DirectedAcyclicGraph</a> in which the links (the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> link) represent a probabilistic conditional dependence between the directly linked nodes. Such a network may be established by asserting (or concluding) <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> predications linking pairs of propositions. There is a 'closed-world assumption' for every <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>, in that pairs of propositions not explicitly linked with <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> are assumed to be not linked. In addition, a node is <a href="http://sw.opencyc.org/concept/Mx4rvX2CRJwpEbGdrcN5Y29ycA" class="cyc_term">conditionallyIndependentSentences</a> the truth values of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (and no other nodes) - from all nodes other than its 'descendants' in the <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a representation of the entire (strictly positive) joint probability distribution over the random variables. The <a href="http://sw.opencyc.org/concept/Mx4rvi7c9JwpEbGdrcN5Y29ycA" class="cyc_term">derivedProbability</a> of a node can be calculated from the probabilities of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s. (There are always one or more 'source' nodes with no <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s.) Theoretically, viewed as evidential links based on the joint probability distribution, the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> links are bidirectional. The direction of the links is obtained formally due to an asymmetry between 'parents' and 'children': the truth of a node induces a conditional dependence among its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (the 'explaining away' effect), which does not seem to apply to its Bayesian 'child' nodes. Most Bayesian network theorists consider that the directions on the links correspond to the direction of causal influence, and hence to the direction of time. The name 'Bayesian' is due to the Reverend Thomas Bayes, whose inversion rule was published posthumously in 1763, and later developed by Laplace. Bayesian Networks were devised chiefly by Judea Pearl in the 1980s. bayes nets Bayesian network http://en.wikipedia.org/wiki/Bayesian_network CarEngineStarting Car Engine Starting Wikipedia Article URL (<a href="http://sw.opencyc.org/concept/Mx4rNv0nbm4TTjOp7yhmnzOyqg" class="cyc_term">wikipediaArticleURL</a> THING URL) means that in <a href="http://sw.opencyc.org/concept/Mx4rtqXA6OC8QdiWC72DuLJdUw" class="cyc_term">Wikipedia_WebSite</a> THING is described by an article located at URL wikipediaArticleURL bayes net The collection of all Bayesian Networks intended for probability reasoning. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a network of nodes in which the nodes are random variables that each typically represent the likelihood of a proposition being true (expressed as a real number between zero and one, where zero means certainly false and one means certainly true). See <a href="http://sw.opencyc.org/concept/Mx4rvtSZipwpEbGdrcN5Y29ycA" class="cyc_term">bayesNetOfMicrotheory</a>. Each <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> interconnects a set of propositions (or symbols associated with propositions) together forming a <a href="http://sw.opencyc.org/concept/Mx4rvtXKWZwpEbGdrcN5Y29ycA" class="cyc_term">DirectedAcyclicGraph</a> in which the links (the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> link) represent a probabilistic conditional dependence between the directly linked nodes. Such a network may be established by asserting (or concluding) <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> predications linking pairs of propositions. There is a 'closed-world assumption' for every <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>, in that pairs of propositions not explicitly linked with <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> are assumed to be not linked. In addition, a node is <a href="http://sw.opencyc.org/concept/Mx4rvX2CRJwpEbGdrcN5Y29ycA" class="cyc_term">conditionallyIndependentSentences</a> the truth values of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (and no other nodes) - from all nodes other than its 'descendants' in the <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a representation of the entire (strictly positive) joint probability distribution over the random variables. The <a href="http://sw.opencyc.org/concept/Mx4rvi7c9JwpEbGdrcN5Y29ycA" class="cyc_term">derivedProbability</a> of a node can be calculated from the probabilities of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s. (There are always one or more 'source' nodes with no <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s.) Theoretically, viewed as evidential links based on the joint probability distribution, the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> links are bidirectional. The direction of the links is obtained formally due to an asymmetry between 'parents' and 'children': the truth of a node induces a conditional dependence among its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (the 'explaining away' effect), which does not seem to apply to its Bayesian 'child' nodes. Most Bayesian network theorists consider that the directions on the links correspond to the direction of causal influence, and hence to the direction of time. The name 'Bayesian' is due to the Reverend Thomas Bayes, whose inversion rule was published posthumously in 1763, and later developed by Laplace. Bayesian Networks were devised chiefly by Judea Pearl in the 1980s. BayesNet bayes net The collection of all Bayesian Networks intended for probability reasoning. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a network of nodes in which the nodes are random variables that each typically represent the likelihood of a proposition being true (expressed as a real number between zero and one, where zero means certainly false and one means certainly true). See <a href="http://sw.opencyc.org/concept/Mx4rvtSZipwpEbGdrcN5Y29ycA" class="cyc_term">bayesNetOfMicrotheory</a>. Each <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> interconnects a set of propositions (or symbols associated with propositions) together forming a <a href="http://sw.opencyc.org/concept/Mx4rvtXKWZwpEbGdrcN5Y29ycA" class="cyc_term">DirectedAcyclicGraph</a> in which the links (the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> link) represent a probabilistic conditional dependence between the directly linked nodes. Such a network may be established by asserting (or concluding) <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> predications linking pairs of propositions. There is a 'closed-world assumption' for every <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>, in that pairs of propositions not explicitly linked with <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> are assumed to be not linked. In addition, a node is <a href="http://sw.opencyc.org/concept/Mx4rvX2CRJwpEbGdrcN5Y29ycA" class="cyc_term">conditionallyIndependentSentences</a> the truth values of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (and no other nodes) - from all nodes other than its 'descendants' in the <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a representation of the entire (strictly positive) joint probability distribution over the random variables. The <a href="http://sw.opencyc.org/concept/Mx4rvi7c9JwpEbGdrcN5Y29ycA" class="cyc_term">derivedProbability</a> of a node can be calculated from the probabilities of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s. (There are always one or more 'source' nodes with no <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s.) Theoretically, viewed as evidential links based on the joint probability distribution, the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> links are bidirectional. The direction of the links is obtained formally due to an asymmetry between 'parents' and 'children': the truth of a node induces a conditional dependence among its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (the 'explaining away' effect), which does not seem to apply to its Bayesian 'child' nodes. Most Bayesian network theorists consider that the directions on the links correspond to the direction of causal influence, and hence to the direction of time. The name 'Bayesian' is due to the Reverend Thomas Bayes, whose inversion rule was published posthumously in 1763, and later developed by Laplace. Bayesian Networks were devised chiefly by Judea Pearl in the 1980s. BayesNet Pretty String (<a href="http://sw.opencyc.org/concept/Mx4rwLSVCpwpEbGdrcN5Y29ycA" class="cyc_term">prettyString</a> TERM STRING) means that STRING is the English word or expression (sequence of words) commonly used to refer to TERM. The predicate <a href="http://sw.opencyc.org/concept/Mx4rwLSVCpwpEbGdrcN5Y29ycA" class="cyc_term">prettyString</a> is used by the code which generates CycL to English paraphrases, but its applicability is not restricted to this use. prettyString probability-topic A <a href="http://sw.opencyc.org/concept/Mx4rAmoSCGJbQdiSXZJvYiNhkQ" class="cyc_term">CycVocabularyTopic</a> and a <a href="http://sw.opencyc.org/concept/Mx4rtGXkHpNaEdqAAAACs0uFOQ" class="cyc_term">KBDependentCollection</a>. Probability-Topic quotedIsa A binary <a href="http://sw.opencyc.org/concept/Mx4rDeDIGEW0EdaAAACgydogAg" class="cyc_term">MetaLanguagePredicate</a> (q.v.) that relates CycL expressions to the <a href="http://sw.opencyc.org/concept/Mx4raOoTci9qEdma4AACs1uxFw" class="cyc_term">SubLExpressionType</a>s (q.v.) of which they are instances. <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> is thus like a restricted version of <a href="http://sw.opencyc.org/concept/Mx4rvViBBJwpEbGdrcN5Y29ycA" class="cyc_term">isa</a> (q.v.), but with one important difference: the first argument-place of <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> is &quot;implicitly quoted&quot; (see <a href="http://sw.opencyc.org/concept/Mx4rwHb_bZwpEbGdrcN5Y29ycA" class="cyc_term">quotedArgument</a>). So a ground atomic sentence of the form <code>(<a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> <b>THING</b> <b>EXPR-TYPE</b>)</code> does <i>not</i> mean that <code><b>THING</b></code> itself is an instance of <code><b>EXPR-TYPE</b></code>. Rather, such a sentence is partly self-referential, and means that the particular <i>CycL expression</i> appearing in the sentence's own first argument-position is an instance of <code><b>EXPR-TYPE</b></code>. Thus, <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> provides a convenient shorthand for stating certain things that would otherwise require explicit quotation (or some other device for naming expressions). <p/> This is better illustrated with a specific example. Suppose we wish to state that the CycL constant <code><a href="http://sw.opencyc.org/concept/Mx4rvViH35wpEbGdrcN5Y29ycA" class="cyc_term">IndianOcean</a></code> is an instance of the CycL expression type <font color="#ff0000">#$PublicConstant</font>. We cannot express this with the straightforward <a href="http://sw.opencyc.org/concept/Mx4rvViBBJwpEbGdrcN5Y29ycA" class="cyc_term">isa</a> sentence <code>(<a href="http://sw.opencyc.org/concept/Mx4rvViBBJwpEbGdrcN5Y29ycA" class="cyc_term">isa</a> <a href="http://sw.opencyc.org/concept/Mx4rvViH35wpEbGdrcN5Y29ycA" class="cyc_term">IndianOcean</a> <font color="#ff0000">#$PublicConstant</font>)</code>, as that states the falsehood that the IndianOcean itself -- which is <i>not</i> a constant but a body of water -- is a public constant. But we can express precisely what we want like this: <pre> (<a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> <a href="http://sw.opencyc.org/concept/Mx4rvViH35wpEbGdrcN5Y29ycA" class="cyc_term">IndianOcean</a> <font color="#ff0000">#$PublicConstant</font>) . </pre> Now <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> is a <a href="http://sw.opencyc.org/concept/Mx4rv4oJWZwpEbGdrcN5Y29ycA" class="cyc_term">MacroRelation</a> (q.v.), and by its <a href="http://sw.opencyc.org/concept/Mx4rvVjg7JwpEbGdrcN5Y29ycA" class="cyc_term">expansion</a> any given <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> sentence is equivalent to some <a href="http://sw.opencyc.org/concept/Mx4rvViBBJwpEbGdrcN5Y29ycA" class="cyc_term">isa</a> sentence with an <i>explicitly</i> quoted first argument. The sentence displayed above turns out to be equivalent to: <pre> (<a href="http://sw.opencyc.org/concept/Mx4rvViBBJwpEbGdrcN5Y29ycA" class="cyc_term">isa</a> (<a href="http://sw.opencyc.org/concept/Mx4rgGBbEkNuEdaAAACgydogAg" class="cyc_term">Quote</a> <a href="http://sw.opencyc.org/concept/Mx4rvViH35wpEbGdrcN5Y29ycA" class="cyc_term">IndianOcean</a>) <font color="#ff0000">#$PublicConstant</font>). </pre> But the <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> version has two related practical advantages over the <a href="http://sw.opencyc.org/concept/Mx4rvViBBJwpEbGdrcN5Y29ycA" class="cyc_term">isa</a> version. First, the former is syntactically simpler than the latter. Second, the simpler syntax of the former makes it easier to browse in the Knowedge Base: while the <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> version is conveniently indexed under the KB Browser page for the constant <code><a href="http://sw.opencyc.org/concept/Mx4rvViH35wpEbGdrcN5Y29ycA" class="cyc_term">IndianOcean</a></code>, the <a href="http://sw.opencyc.org/concept/Mx4rvViBBJwpEbGdrcN5Y29ycA" class="cyc_term">isa</a> version would apparently be indexed under a separate, brand new page for the term <code>(<a href="http://sw.opencyc.org/concept/Mx4rgGBbEkNuEdaAAACgydogAg" class="cyc_term">Quote</a> <a href="http://sw.opencyc.org/concept/Mx4rvViH35wpEbGdrcN5Y29ycA" class="cyc_term">IndianOcean</a>)</code>. Generalizing the point, <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a> lets us avoid having potentially to double the number of pages currently in the browser. <p/> For the semantically more complicated (but rarely encountered) case in which <a href="http://sw.opencyc.org/concept/Mx4rBVVEokNxEdaAAACgydogAg" class="cyc_term">quotedIsa</a>'s first argument-place is filled with an <i>open</i> expression, see the accompanying <font color="#ff0000">#$cyclistNotes</font>. Quoted Isa The collection of those Cyc constants (individuals, collections, predicates, functions, and non-atomic terms) created to enable probabilistic reasoning (of various kinds). CycLProbabilityConstant probabilistic constant bayes net The collection of all Bayesian Networks intended for probability reasoning. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a network of nodes in which the nodes are random variables that each typically represent the likelihood of a proposition being true (expressed as a real number between zero and one, where zero means certainly false and one means certainly true). See <a href="http://sw.opencyc.org/concept/Mx4rvtSZipwpEbGdrcN5Y29ycA" class="cyc_term">bayesNetOfMicrotheory</a>. Each <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> interconnects a set of propositions (or symbols associated with propositions) together forming a <a href="http://sw.opencyc.org/concept/Mx4rvtXKWZwpEbGdrcN5Y29ycA" class="cyc_term">DirectedAcyclicGraph</a> in which the links (the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> link) represent a probabilistic conditional dependence between the directly linked nodes. Such a network may be established by asserting (or concluding) <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> predications linking pairs of propositions. There is a 'closed-world assumption' for every <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>, in that pairs of propositions not explicitly linked with <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> are assumed to be not linked. In addition, a node is <a href="http://sw.opencyc.org/concept/Mx4rvX2CRJwpEbGdrcN5Y29ycA" class="cyc_term">conditionallyIndependentSentences</a> the truth values of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (and no other nodes) - from all nodes other than its 'descendants' in the <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a>. A <a href="http://sw.opencyc.org/concept/Mx4rvhSzKJwpEbGdrcN5Y29ycA" class="cyc_term">BayesNet</a> is a representation of the entire (strictly positive) joint probability distribution over the random variables. The <a href="http://sw.opencyc.org/concept/Mx4rvi7c9JwpEbGdrcN5Y29ycA" class="cyc_term">derivedProbability</a> of a node can be calculated from the probabilities of its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s. (There are always one or more 'source' nodes with no <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s.) Theoretically, viewed as evidential links based on the joint probability distribution, the <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a> links are bidirectional. The direction of the links is obtained formally due to an asymmetry between 'parents' and 'children': the truth of a node induces a conditional dependence among its <a href="http://sw.opencyc.org/concept/Mx4rvbvRRJwpEbGdrcN5Y29ycA" class="cyc_term">bayesParent</a>s (the 'explaining away' effect), which does not seem to apply to its Bayesian 'child' nodes. Most Bayesian network theorists consider that the directions on the links correspond to the direction of causal influence, and hence to the direction of time. The name 'Bayesian' is due to the Reverend Thomas Bayes, whose inversion rule was published posthumously in 1763, and later developed by Laplace. Bayesian Networks were devised chiefly by Judea Pearl in the 1980s. BayesNet wikipediaArticleName (<a href="http://sw.opencyc.org/concept/Mx4rTv-jk9SPTXa991kk5mAvHg" class="cyc_term">wikipediaArticleName</a> THING NAME) means that in <a href="http://sw.opencyc.org/concept/Mx4rtqXA6OC8QdiWC72DuLJdUw" class="cyc_term">Wikipedia_WebSite</a> THING is described by an article with the title NAME Wikipedia Article Name The collection of all those <a href="http://sw.opencyc.org/concept/Mx4rvrPfJpwpEbGdrcN5Y29ycA" class="cyc_term">DirectedGraph</a>s (node-and-link structures in which each link has one direction) each of which has no directed cycle in it. This is the intersection of <a href="http://sw.opencyc.org/concept/Mx4rvrPfJpwpEbGdrcN5Y29ycA" class="cyc_term">DirectedGraph</a> and <a href="http://sw.opencyc.org/concept/Mx4rvdnP8ZwpEbGdrcN5Y29ycA" class="cyc_term">DirectedAcyclicPathSystem</a> (which is the same as the intersection of <a href="http://sw.opencyc.org/concept/Mx4rviabPZwpEbGdrcN5Y29ycA" class="cyc_term">SimpleGraph_GraphTheoretic</a> and <a href="http://sw.opencyc.org/concept/Mx4rvdnP8ZwpEbGdrcN5Y29ycA" class="cyc_term">DirectedAcyclicPathSystem</a>). A <a href="http://sw.opencyc.org/concept/Mx4rvtXKWZwpEbGdrcN5Y29ycA" class="cyc_term">DirectedAcyclicGraph</a> is often used as a representation of a <a href="http://sw.opencyc.org/concept/Mx4rwTWq1ZwpEbGdrcN5Y29ycA" class="cyc_term">PartialOrdering</a>. DirectedAcyclicGraph Directed Acyclic Graph