Master’s theses by Jasper De Bock and Arthur Van Camp

by gertekoo

I have been telling you about work Jasper De Bock and I have done on state sequence prediction in imprecise hidden Markov Models, leading to the development of the EstiHMM algorithm. Now, Jasper’s master’s thesis (written in Dutch with an English extended abstract) on this subject has been submitted, and is available for download. We have submitted a paper about this to the ISIPTA 2011 conference.

Arthur Van Camp has been working on applying the MePiCTIr algorithm to inference in imprecise Hidden Markov models, with a simple but interesting application in earthquake rate prediction. Hidden in his text is an interesting idea about the interplay between quantisation (or discretisation) and imprecision I have been toying with for some time now, and hope to be able to work on with him in the coming year. Arthur has submitted an abstract for poster presentation at ISIPTA 2011. His master’s thesis (written in Dutch with an English extended abstract) on this subject has been submitted, and is available for download too.


Here are English titles and abstracts for both:

EstiHMM: an efficient algorithm for state sequence prediction in imprecise hidden Markov models by Jasper De Bock

Abstract: We develop an efficient algorithm that calculates the maximal state sequences in an imprecise hidden Markov model by means of coherent lower previsions. Initial results show that this algorithm is able to robustify the inferences made by a classical precise model.

Hidden Markov models: A proof of concept for the MePICTIr algorithm by Arthur Van Camp

Abstract: The combination of Bayesian networks with the theory of imprecise probabilities leads to so-called credal nets. Credal nets are very useful, especially when there is little data. We use the MePICTIr algorithm to make inferences and to perform experiments in imprecise hidden Markov models, a type of credal nets. We present a method for learning imprecise transition models in hidden Markov models We apply our method to build an imprecise hidden Markov model with real data of earthquake counts per year, and to make predictions of this rate for future years using the MePICTIr algorithm.

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