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• W127549163 abstract "In recent years, graphical models have been successfully applied in several differentdisciplines, including medicine, biology and epidemiology. This has been made possibleby the rapid evolution of structure learning algorithms, from constraint-based ones toscore-based and hybrid ones. The main goal in the development of these algorithmshas been the reduction of the number of either independence tests or score comparisonsneeded to learn the structure of the Bayesian network. In most cases the characteristicsof the learned networks have been studied using a small number of reference data setsas benchmarks, and differences from the true structure heve been measured with purelydescriptive measures such as Hamming distance. This approach to model validation is not possible for real world data sets, as the truestructure of their probability distribution is not known. An alternative is providedby the use of either parametric or nonparametric bootstrap. By applying a learningalgorithm to a sufficiently large number of bootstrap samples it is possible to obtain theempirical probability of any feature of the resulting network, such as the structure ofthe Markov Blanket of a particular node. The fundamental limit in the interpretationof the results is that the “reasonable” level of confidence for thresholding depends onthe data and the learning algorithm. In this thesis we extend the aforementioned bootstrap-based approach for the in-ference on the structure of a Bayesian or Markov network. The graph representingthe network structure and its underlying undirected graph (in the case of Bayesiannetworks) are modelled using a multivariate extension of the Trinomial and Bernoullidistributions; each component is associated with an arc. These assumptions allow thederivation of exact and asymptotic measures of the variability of the network structureor any of its parts. These measures are then applied to some common learning strate-gies used in literature using the implementation provided by the bnlearn R packageimplemented and maintained by the author." @default.
• W127549163 created "2016-06-24" @default.
• W127549163 creator A5059820079 @default.
• W127549163 date "2011-01-18" @default.
• W127549163 modified "2023-09-26" @default.
• W127549163 title "Measures of Variability for Graphical Models" @default.
• W127549163 cites W1494413412 @default.
• W127549163 cites W1502461990 @default.
• W127549163 cites W1511986666 @default.
• W127549163 cites W1524326598 @default.
• W127549163 cites W1530964327 @default.
• W127549163 cites W1544685773 @default.
• W127549163 cites W1554944419 @default.
• W127549163 cites W1556681549 @default.
• W127549163 cites W1568555062 @default.
• W127549163 cites W1584444527 @default.
• W127549163 cites W1590398161 @default.
• W127549163 cites W1614514575 @default.
• W127549163 cites W1626295059 @default.
• W127549163 cites W1667965753 @default.
• W127549163 cites W1668763905 @default.
• W127549163 cites W1676756873 @default.
• W127549163 cites W1755360231 @default.
• W127549163 cites W1769824028 @default.
• W127549163 cites W1801737117 @default.
• W127549163 cites W1830407698 @default.
• W127549163 cites W1886215955 @default.
• W127549163 cites W1970687027 @default.
• W127549163 cites W1972565657 @default.
• W127549163 cites W1980404085 @default.
• W127549163 cites W1982547531 @default.
• W127549163 cites W1990512452 @default.
• W127549163 cites W1995945562 @default.
• W127549163 cites W2002374079 @default.
• W127549163 cites W2003602339 @default.
• W127549163 cites W2007834077 @default.
• W127549163 cites W2008906462 @default.
• W127549163 cites W2011344606 @default.
• W127549163 cites W2015497290 @default.
• W127549163 cites W2034230784 @default.
• W127549163 cites W2049633694 @default.
• W127549163 cites W2056099894 @default.
• W127549163 cites W2058514264 @default.
• W127549163 cites W2058653097 @default.
• W127549163 cites W2063698478 @default.
• W127549163 cites W2068047481 @default.
• W127549163 cites W2069032421 @default.
• W127549163 cites W2074673068 @default.
• W127549163 cites W2075032250 @default.
• W127549163 cites W2099111195 @default.
• W127549163 cites W2105480138 @default.
• W127549163 cites W2120350343 @default.
• W127549163 cites W2123838014 @default.
• W127549163 cites W2128088446 @default.
• W127549163 cites W2130695933 @default.
• W127549163 cites W2132555912 @default.
• W127549163 cites W2135191607 @default.
• W127549163 cites W2142635246 @default.
• W127549163 cites W2143891888 @default.
• W127549163 cites W2144731007 @default.
• W127549163 cites W2144907232 @default.
• W127549163 cites W2145569759 @default.
• W127549163 cites W2146521249 @default.
• W127549163 cites W2149337551 @default.
• W127549163 cites W2157611962 @default.
• W127549163 cites W2159080219 @default.
• W127549163 cites W2161205483 @default.
• W127549163 cites W2161369142 @default.
• W127549163 cites W2161642932 @default.
• W127549163 cites W2165190832 @default.
• W127549163 cites W2165408259 @default.
• W127549163 cites W2168175751 @default.
• W127549163 cites W2169004268 @default.
• W127549163 cites W2170112109 @default.
• W127549163 cites W2172192274 @default.
• W127549163 cites W2767905780 @default.
• W127549163 cites W2798461040 @default.
• W127549163 cites W2799004609 @default.
• W127549163 cites W29105686 @default.
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• W127549163 cites W58491437 @default.
• W127549163 cites W601863383 @default.
• W127549163 cites W603456503 @default.
• W127549163 cites W614670535 @default.
• W127549163 cites W633139985 @default.
• W127549163 cites W2739965505 @default.
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