ºÝºÝߣshows by User: Bayesian-Intelligence / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: Bayesian-Intelligence / Fri, 15 Jan 2016 01:03:03 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: Bayesian-Intelligence Omnigram explorer: an interactive tool for understanding Bayesian networks /slideshow/omnigram-explorer-an-interactive-tool-for-understanding-bayesian-networks/57078352 abnms2015-korbetal-160115010303
We describe the design of Omnigram Explorer (OMG), an open-source tool for the interactive exploration of relationships between variables in Bayesian networks (and other complex systems). OMG is designed to help researchers gain a holistic, qualitative understanding of the relationships between variables, specifically providing interactive, visual support for observational sensitivity analysis. OMG is especially useful for high-lighting dependencies between variables and small groups of variables. It's designed for exploratory analysis of BNs (and other models) and for communicating the salient features of models to non-specialists. Kevin Korb - http://www.csse.monash.edu.au/~korb Tim Taylor - http://www.tim-taylor.com/ Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]>
We describe the design of Omnigram Explorer (OMG), an open-source tool for the interactive exploration of relationships between variables in Bayesian networks (and other complex systems). OMG is designed to help researchers gain a holistic, qualitative understanding of the relationships between variables, specifically providing interactive, visual support for observational sensitivity analysis. OMG is especially useful for high-lighting dependencies between variables and small groups of variables. It's designed for exploratory analysis of BNs (and other models) and for communicating the salient features of models to non-specialists. Kevin Korb - http://www.csse.monash.edu.au/~korb Tim Taylor - http://www.tim-taylor.com/ Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]> Fri, 15 Jan 2016 01:03:03 GMT /slideshow/omnigram-explorer-an-interactive-tool-for-understanding-bayesian-networks/57078352 Bayesian-Intelligence@slideshare.net(Bayesian-Intelligence) Omnigram explorer: an interactive tool for understanding Bayesian networks Bayesian-Intelligence We describe the design of Omnigram Explorer (OMG), an open-source tool for the interactive exploration of relationships between variables in Bayesian networks (and other complex systems). OMG is designed to help researchers gain a holistic, qualitative understanding of the relationships between variables, specifically providing interactive, visual support for observational sensitivity analysis. OMG is especially useful for high-lighting dependencies between variables and small groups of variables. It's designed for exploratory analysis of BNs (and other models) and for communicating the salient features of models to non-specialists. Kevin Korb - http://www.csse.monash.edu.au/~korb Tim Taylor - http://www.tim-taylor.com/ Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-korbetal-160115010303-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We describe the design of Omnigram Explorer (OMG), an open-source tool for the interactive exploration of relationships between variables in Bayesian networks (and other complex systems). OMG is designed to help researchers gain a holistic, qualitative understanding of the relationships between variables, specifically providing interactive, visual support for observational sensitivity analysis. OMG is especially useful for high-lighting dependencies between variables and small groups of variables. It&#39;s designed for exploratory analysis of BNs (and other models) and for communicating the salient features of models to non-specialists. Kevin Korb - http://www.csse.monash.edu.au/~korb Tim Taylor - http://www.tim-taylor.com/ Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/
Omnigram explorer: an interactive tool for understanding Bayesian networks from Bayesian Intelligence
]]> 422 4 https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-korbetal-160115010303-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Dependency patterns for latent variable discovery /slideshow/dependency-patterns-for-latent-variable-discovery/57077998 abnms2015-zhangetal-160115004306
The causal discovery of Bayesian networks with the presence of latent variables is a popular topic in artificial intelligence, as sources and volumes of data continue to grow with the popularity of Bayesian modelling methods. Causal discovery is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. Frequently, however, some of those dependencies are generated by causal structures involving variables which have not been measured, i.e., latent variables. Some such patterns of dependency “reveal" themselves, in that no model based solely upon the observed variables can explain them as well as a model using a latent variable. Here we present an algorithm for finding such patterns systematically, so that they may be applied in latent variable discovery in a more rigorous fashion. Xuhui Zhang, Kevin Korb, Ann Nicholson and Steven Mascaro Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]>
The causal discovery of Bayesian networks with the presence of latent variables is a popular topic in artificial intelligence, as sources and volumes of data continue to grow with the popularity of Bayesian modelling methods. Causal discovery is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. Frequently, however, some of those dependencies are generated by causal structures involving variables which have not been measured, i.e., latent variables. Some such patterns of dependency “reveal" themselves, in that no model based solely upon the observed variables can explain them as well as a model using a latent variable. Here we present an algorithm for finding such patterns systematically, so that they may be applied in latent variable discovery in a more rigorous fashion. Xuhui Zhang, Kevin Korb, Ann Nicholson and Steven Mascaro Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]> Fri, 15 Jan 2016 00:43:06 GMT /slideshow/dependency-patterns-for-latent-variable-discovery/57077998 Bayesian-Intelligence@slideshare.net(Bayesian-Intelligence) Dependency patterns for latent variable discovery Bayesian-Intelligence The causal discovery of Bayesian networks with the presence of latent variables is a popular topic in artificial intelligence, as sources and volumes of data continue to grow with the popularity of Bayesian modelling methods. Causal discovery is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. Frequently, however, some of those dependencies are generated by causal structures involving variables which have not been measured, i.e., latent variables. Some such patterns of dependency “reveal" themselves, in that no model based solely upon the observed variables can explain them as well as a model using a latent variable. Here we present an algorithm for finding such patterns systematically, so that they may be applied in latent variable discovery in a more rigorous fashion. Xuhui Zhang, Kevin Korb, Ann Nicholson and Steven Mascaro Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-zhangetal-160115004306-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The causal discovery of Bayesian networks with the presence of latent variables is a popular topic in artificial intelligence, as sources and volumes of data continue to grow with the popularity of Bayesian modelling methods. Causal discovery is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. Frequently, however, some of those dependencies are generated by causal structures involving variables which have not been measured, i.e., latent variables. Some such patterns of dependency “reveal&quot; themselves, in that no model based solely upon the observed variables can explain them as well as a model using a latent variable. Here we present an algorithm for finding such patterns systematically, so that they may be applied in latent variable discovery in a more rigorous fashion. Xuhui Zhang, Kevin Korb, Ann Nicholson and Steven Mascaro Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/
Dependency patterns for latent variable discovery from Bayesian Intelligence
]]> 938 9 https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-zhangetal-160115004306-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Markov Blanket Causal Discovery Using Minimum Message Length /slideshow/markov-blanket-causal-discovery-using-minimum-message-length/57077657 abnms2015-li-markovblanketcausaldiscoveryusingmml-160115002418
Learning a causal network over a set of variables from data is NP-hard and is exponential in the number of variables. State-of-the-art causal discovery algorithms do not scale up well in high-dimensional datasets. Recently, a technique called Markov blanket causal discovery was proposed to increase efficiencies of state-of-the-art algorithms and hence scale up to larger networks. This presentation provides an introduction to causal discovery problems using the Markov blanket technique and minimum message length (MML) to search for the most optimal causal network of a given dataset. Kelvin Yang Li Supervisors: Dr Kevin Korb, Dr Lloyd Allison, Dr Francois Petitjean Monash University - November 25, 2015 Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]>
Learning a causal network over a set of variables from data is NP-hard and is exponential in the number of variables. State-of-the-art causal discovery algorithms do not scale up well in high-dimensional datasets. Recently, a technique called Markov blanket causal discovery was proposed to increase efficiencies of state-of-the-art algorithms and hence scale up to larger networks. This presentation provides an introduction to causal discovery problems using the Markov blanket technique and minimum message length (MML) to search for the most optimal causal network of a given dataset. Kelvin Yang Li Supervisors: Dr Kevin Korb, Dr Lloyd Allison, Dr Francois Petitjean Monash University - November 25, 2015 Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]> Fri, 15 Jan 2016 00:24:18 GMT /slideshow/markov-blanket-causal-discovery-using-minimum-message-length/57077657 Bayesian-Intelligence@slideshare.net(Bayesian-Intelligence) Markov Blanket Causal Discovery Using Minimum Message Length Bayesian-Intelligence Learning a causal network over a set of variables from data is NP-hard and is exponential in the number of variables. State-of-the-art causal discovery algorithms do not scale up well in high-dimensional datasets. Recently, a technique called Markov blanket causal discovery was proposed to increase efficiencies of state-of-the-art algorithms and hence scale up to larger networks. This presentation provides an introduction to causal discovery problems using the Markov blanket technique and minimum message length (MML) to search for the most optimal causal network of a given dataset. Kelvin Yang Li Supervisors: Dr Kevin Korb, Dr Lloyd Allison, Dr Francois Petitjean Monash University - November 25, 2015 Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-li-markovblanketcausaldiscoveryusingmml-160115002418-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Learning a causal network over a set of variables from data is NP-hard and is exponential in the number of variables. State-of-the-art causal discovery algorithms do not scale up well in high-dimensional datasets. Recently, a technique called Markov blanket causal discovery was proposed to increase efficiencies of state-of-the-art algorithms and hence scale up to larger networks. This presentation provides an introduction to causal discovery problems using the Markov blanket technique and minimum message length (MML) to search for the most optimal causal network of a given dataset. Kelvin Yang Li Supervisors: Dr Kevin Korb, Dr Lloyd Allison, Dr Francois Petitjean Monash University - November 25, 2015 Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/
Markov Blanket Causal Discovery Using Minimum Message Length from Bayesian Intelligence
]]> 871 5 https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-li-markovblanketcausaldiscoveryusingmml-160115002418-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-Bayesian-Intelligence-48x48.jpg?cb=1523414843 bayesian-intelligence.com https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-korbetal-160115010303-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/omnigram-explorer-an-interactive-tool-for-understanding-bayesian-networks/57078352 Omnigram explorer: an... https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-zhangetal-160115004306-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/dependency-patterns-for-latent-variable-discovery/57077998 Dependency patterns fo... https://cdn.slidesharecdn.com/ss_thumbnails/abnms2015-li-markovblanketcausaldiscoveryusingmml-160115002418-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/markov-blanket-causal-discovery-using-minimum-message-length/57077657 Markov Blanket Causal ...