際際滷shows by User: alessandroantonucci1 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: alessandroantonucci1 / Tue, 21 May 2019 12:57:07 GMT 際際滷Share feed for 際際滷shows by User: alessandroantonucci1 Flairs2019 antonucci /slideshow/flairs2019-antonucci/146903856 flairs2019antonucci-190521125707
RELIABLE DISCRETISATION OF DETERMINISTIC EQUATIONS IN BAYESIAN NETWORKS]]>

RELIABLE DISCRETISATION OF DETERMINISTIC EQUATIONS IN BAYESIAN NETWORKS]]>
Tue, 21 May 2019 12:57:07 GMT /slideshow/flairs2019-antonucci/146903856 alessandroantonucci1@slideshare.net(alessandroantonucci1) Flairs2019 antonucci alessandroantonucci1 RELIABLE DISCRETISATION OF DETERMINISTIC EQUATIONS IN BAYESIAN NETWORKS <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/flairs2019antonucci-190521125707-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> RELIABLE DISCRETISATION OF DETERMINISTIC EQUATIONS IN BAYESIAN NETWORKS
Flairs2019 antonucci from Alessandro Antonucci
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Decision Making with Hierarchical Credal Sets (IPMU 2014) /slideshow/ipmu2014-slides/37120462 ipmu2014slides-140718051757-phpapp01
We elaborate on hierarchical credal sets, which are sets of probability mass functions paired with second-order distributions. A new criterion to make decisions based on these models is proposed. This is achieved by sampling from the set of mass functions and considering the Kullback-Leibler divergence from the weighted center of mass of the set. We evaluate this criterion in a simple classification scenario: the results show performance improvements when compared to a credal classifier where the second-order distribution is not taken into account. ]]>

We elaborate on hierarchical credal sets, which are sets of probability mass functions paired with second-order distributions. A new criterion to make decisions based on these models is proposed. This is achieved by sampling from the set of mass functions and considering the Kullback-Leibler divergence from the weighted center of mass of the set. We evaluate this criterion in a simple classification scenario: the results show performance improvements when compared to a credal classifier where the second-order distribution is not taken into account. ]]>
Fri, 18 Jul 2014 05:17:57 GMT /slideshow/ipmu2014-slides/37120462 alessandroantonucci1@slideshare.net(alessandroantonucci1) Decision Making with Hierarchical Credal Sets (IPMU 2014) alessandroantonucci1 We elaborate on hierarchical credal sets, which are sets of probability mass functions paired with second-order distributions. A new criterion to make decisions based on these models is proposed. This is achieved by sampling from the set of mass functions and considering the Kullback-Leibler divergence from the weighted center of mass of the set. We evaluate this criterion in a simple classification scenario: the results show performance improvements when compared to a credal classifier where the second-order distribution is not taken into account. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ipmu2014slides-140718051757-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We elaborate on hierarchical credal sets, which are sets of probability mass functions paired with second-order distributions. A new criterion to make decisions based on these models is proposed. This is achieved by sampling from the set of mass functions and considering the Kullback-Leibler divergence from the weighted center of mass of the set. We evaluate this criterion in a simple classification scenario: the results show performance improvements when compared to a credal classifier where the second-order distribution is not taken into account.
Decision Making with Hierarchical Credal Sets (IPMU 2014) from Alessandro Antonucci
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