The Society for Imprecise Probabilities:
Theories and Applications

Blog category: theses

These are all the posts in the theses category of the SIPTA blog, but you can also view all blog posts.

Dominik Hose’s PhD thesis on Possibilistic Reasoning with Imprecise Probabilities

Posted on April 11, 2023 in theses by Dominik Hose (edited by Henna Bains)

On May 20th in 2022, I successfully defended my PhD thesis [1] entitled “Possibilistic Reasoning with Imprecise Probabilities: Statistical Inference and Dynamic Filtering”. This dissertation is the result of five wonderful years at the Institute of Engineering and Computational Mechanics at the University of Stuttgart under the enthusiastic supervision of my “Doktorvater” Michael Hanss. Apart from him, my committee was also composed of Scott Ferson and Ryan Martin—but we will get to that.

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Tathagata Basu’s PhD thesis on High dimensional statistical modelling under limited information

Posted on September 12, 2022 in theses by Tathagata Basu (edited by Henna Bains)

After three years of research and extensive brainstorming with my supervisors; Jochen Einbeck and Matthias Troffaes, I finally defended my thesis on 15th December 2020. The thesis, entitled “High dimensional statistical modelling under limited information” was examined by Dr Hailiang Du and Dr Erik Quaghebeur in the presence of Dr Ostap Hryniv.

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Jasper De Bock’s PhD thesis on Credal Networks under Epistemic Irrelevance

Posted on March 9, 2016 in theses by Jasper De Bock

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 the key variables in some domain, and which can then be used to answer various domain-specific queries (compute inferences) that are of interest to the user. They share many of the nice features of Pearl’s celebrated Bayesian networks, but have the added advantage that they can represent uncertainty in a more flexible and realistic way.

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Ignacio Montes’ PhD thesis on Comparison of alternatives under Uncertainty and imprecision

Posted on June 24, 2014 in theses by Ignacio Montes

This thesis, supervised by Enrique Miranda and Susana Montes, was defended on May 16th. The jury was composed of Susana Díaz, Serafín Moral and Bernard De Baets. This thesis deals with the problem of comparing alternatives defined under some lack of information, that is considered to be either uncertainty, imprecision or both together.

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Andrea Wiencierz's PhD thesis on Regression analysis with imprecise data

Posted on April 24, 2014 in theses by Andrea Wiencierz

My PhD thesis deals with the statistical problem of analyzing the relationship between a response variable and one or more explanatory variables when these quantities are only imprecisely observed. Regression methods are some of the most popular and commonly employed methods of statistical data analysis. Like most statistical tools, regression methods are usually based on the assumption that the analyzed data are precise and correct observations of the variables of interest. In statistical practice, however, often only incomplete or uncertain information about the data values is available.

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Denis D. Mauá's PhD Thesis on Algorithms and Complexity Results for Discrete Probabilistic Reasoning Tasks

Posted on January 9, 2014 in theses by Dennis D. Mauá

My PhD thesis is about connecting three hard computational problems that arise in tasks involving graph-based probabilistic reasoning, namely, the problems of maximum a posteriori (MAP) inference in Bayesian networks, planning with influence diagrams, and belief updating in credal networks under strong independence (or simply strong credal networks). Roughly speaking, in the MAP inference problem we seek the most probable explanation of a complex phenomena represented as a Bayesian network, a graph-based description of a multivariate joint probability distribution where nodes are identified with random variables and local conditional probability distributions.

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