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/]]>

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/]]>

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/]]>

Fog events occur at Melbourne Airport, Aus- tralia, approximately 12 times each year. Un- forecast events are costly to the aviation industry, cause disruption and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast due to the complexity of the physical processes and the impact of local geography and weather ele- ments. Bayesian networks (BNs) are a probabilistic rea- soning tool widely used for prediction, diagno- sis and risk assessment in a range of application domains. Several BNs for probabilistic weather prediction have been previously reported, but to date none have included an explicit forecast de- cision component and none have been used for operational weather forecasting. A Bayesian De- cision Network (Bayesian Objective Fog Fore- cast Information Network; BOFFIN) has been developed for fog forecasting at Melbourne Air- port based on 34 years of data (1972-2005). Pa- rameters were calibrated to ensure that the net- work had equivalent or better performance to prior operational forecast methods, which lead to its adoption as an operational decision sup- port tool. The operational use of the network by forecasters over an 8 year period (2006-2013) has been evaluated [1], showing significantly im- proved forecasting accuracy by the forecasters using the network, as compared with previous years. BOFFIN-Melbourne has been accepted by forecasters due to its skill, visualisation and explanation facilities, and because it offers fore- casters control over inputs where a predictor is considered unreliable... Paper: http://abnms.org/conferences/abnms2015/papers/ABNMS_2015_paper_16%20-%20A%20Tool%20for%20Visualising%20the%20output%20of%20a%20DBN%20for%20fog%20forecasting.pdf Tali Boneh, Xuhui Zhang, Ann Nicholson and Kevin Korb Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]>

Fog events occur at Melbourne Airport, Aus- tralia, approximately 12 times each year. Un- forecast events are costly to the aviation industry, cause disruption and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast due to the complexity of the physical processes and the impact of local geography and weather ele- ments. Bayesian networks (BNs) are a probabilistic rea- soning tool widely used for prediction, diagno- sis and risk assessment in a range of application domains. Several BNs for probabilistic weather prediction have been previously reported, but to date none have included an explicit forecast de- cision component and none have been used for operational weather forecasting. A Bayesian De- cision Network (Bayesian Objective Fog Fore- cast Information Network; BOFFIN) has been developed for fog forecasting at Melbourne Air- port based on 34 years of data (1972-2005). Pa- rameters were calibrated to ensure that the net- work had equivalent or better performance to prior operational forecast methods, which lead to its adoption as an operational decision sup- port tool. The operational use of the network by forecasters over an 8 year period (2006-2013) has been evaluated [1], showing significantly im- proved forecasting accuracy by the forecasters using the network, as compared with previous years. BOFFIN-Melbourne has been accepted by forecasters due to its skill, visualisation and explanation facilities, and because it offers fore- casters control over inputs where a predictor is considered unreliable... Paper: http://abnms.org/conferences/abnms2015/papers/ABNMS_2015_paper_16%20-%20A%20Tool%20for%20Visualising%20the%20output%20of%20a%20DBN%20for%20fog%20forecasting.pdf Tali Boneh, Xuhui Zhang, Ann Nicholson and Kevin Korb Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/]]>

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