Slideshows for Tag: bayesian-network http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows for Tag: bayesian-network http://www.slideshare.net/ Fri, 30 May 2008 12:52:57 GMT SlideShare feed for Slideshows for Tag: bayesian-network RepèRes Bayesia Consumer Segmentation Skim Conf08 http://www.slideshare.net/f.abiven/repres-bayesia-consumer-segmentation-skim-conf08
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]]> Fri, 30 May 2008 12:52:57 GMT http://www.slideshare.net/f.abiven/repres-bayesia-consumer-segmentation-skim-conf08 f.abiven@slideshare.net(f.abiven) RepèRes Bayesia Consumer Segmentation Skim Conf08 f.abiven <img src="http://cdn.slidesharecdn.com/represbayesia-consumer-segmentation-skim-conf08-1212159131169419-8-thumbnail-2?1212151977" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> RepèRes Bayesia Consumer Segmentation Skim Conf08
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]]> 1804 0 http://cdn.slidesharecdn.com/represbayesia-consumer-segmentation-skim-conf08-1212159131169419-8-thumbnail-2?1212151977 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin Glasses http://www.slideshare.net/pelikan/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses-83852
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.]]>
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.]]> Sat, 28 Jul 2007 11:23:25 GMT http://www.slideshare.net/pelikan/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses-83852 pelikan@slideshare.net(pelikan) Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin Glasses pelikan The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem. <img src="http://cdn.slidesharecdn.com/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses574-thumbnail-2?1185621805" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem. Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin Glasses
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]]> 1339 0 http://cdn.slidesharecdn.com/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses574-thumbnail-2?1185621805 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Order Or Not: Does Parallelization of Model Building in hBOA Affect Its Scalability? http://www.slideshare.net/pelikan/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability
It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible.]]>
It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible.]]> Thu, 26 Jul 2007 05:23:22 GMT http://www.slideshare.net/pelikan/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability pelikan@slideshare.net(pelikan) Order Or Not: Does Parallelization of Model Building in hBOA Affect Its Scalability? pelikan It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible. <img src="http://cdn.slidesharecdn.com/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability1657-thumbnail-2?1185427402" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible. Order Or Not: Does Parallelization of Model Building in hBOA Affect Its Scalability?
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]]> 858 0 http://cdn.slidesharecdn.com/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability1657-thumbnail-2?1185427402 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted
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