A few weeks ago, Michele had a conversation with about his exceptional career, the status of the field of Imprecise Probabilistic Machine Learning, and more. Please enjoy the interview below ..read more

In my thesis, we further explore the potential of desirability theory, a very powerful uncertainty formalism developed within the context of imprecise probabilities, pursuing three primary research directions. Firstly, we provide joint foundations for the fields of social choice and probabilistic opinion pooling using desirability. This strategy offers a fresh perspective on traditional results in those fields and provides a tool for comparing their formulations. Secondly, we examine the relationship between desirability and information algebras. The latter are general algebraic structures int ..read more

The 9th International Symposium on Imprecise Probability: Theories and Applications was held from Monday 20 to Friday 24 July 2015 in the Italian city of Pescara. This conference was a result of the productive cooperation among the members of the Steering  Committee, formed by Thomas Augustin, Gert de Cooman, Enrique Miranda, Erik Quaeghebeur, Teddy Seidenfeld, and myself.  I wish to thank  the members of the Local Organizing Committee, Attilio Grilli, Mariangela Scorrano and Andrea Di Cencio for their valuable help and the Department of Engineering and Geology of the University ..read more

On 13 May 2015, after four years of intensive research under the enthousiastic supervision of Gert de Cooman, I succesfully defended my PhD Thesis, entitled “Credal Networks under Epistemic Irrelevance: Theory and Algorithms”. The jury was composed of Fabio Cozman, Enrique Miranda, Serafín Moral, Joris Walraevens, Dirk Aeyels, Dries Benoit, Jan Van Campenhout and Rik Van de Walle. My dissertation presents a detailed study of credal networks under epistemic irrelevance, which are probabilistic graphical models that can compactly and intuitively represent the uncertainty that is associated with ..read more

Credal networks combine the intuitive expressivity of graphs and the principled treatment and flexibility of imprecise probability to deliver a powerful framework for uncertainty management. Here, we briefly motivate the use of credal networks, provide a historical account of their development and present a commented bibliography on existing surveys and tutorials on the topic ..read more

Posted on June 6, 2021 by Ryan Martin (edited by Ignacio Montes) develops a new framework for statistical inference, one that assumes no prior, but returns (something like) a posterior probability distribution that can be used for drawing inferences. Moreover, that probability-like output satisfies a strong calibration property, called , which, among other things, implies that procedures derived from it provably control frequentist error rates. How is this related to imprecise probability? Indeed, this is not a book about imprecise probability, but it turns out that the inferential model’s pro ..read more

This blog is the first instalment in a new blog series where we interview members of the SIPTA community. A few weeks ago, Henna had a conversation with Sébastien Destercke about his career so far, thoughts on imprecise probabilities, and more. Please enjoy the interview below. Hello, Sébastien. To start, could you give the reader a quick insight into who you are by describing yourself in three words or phrases? This one is difficult; hmm, I’d say jack of all trades, gamer and conversationalist (I like to engage in long conversations about topics of interest ..read more