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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/199420
Estadística freqüentista versus estadística bayesiana: una comparativa amb gràfics d’accessibilitat millorada
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[en] IT engineers widely use statistics nowadays and, more often, Bayesian Statistics. That is because Big Data and other data mining techniques are the day’s order, which is needed to create the a priori beliefs required for this kind of Statistics.
Commonly, degree studies in Mathematics centre on classic Statistics, which means Frequentist, but scarce knowledge about Bayesian Statistics concepts is addressed. So doing a better mathematical analysis of the Bayesian approach appears to be very useful.
That reason has motivated the development of a comparison between these two types of Statistics, which you will find in this research project. First, a more theoretical and general approach is addressed, explaining aspects where they differ. Then, that comparison is analysed by selecting and using a few simple problems that can be described using statistical graphics.
These graphics have been made accessible (in a way that screen readers can manage their information) using two different IT libraries. Then, a Users Test was designed and prepared, and a population sample of blind people
performed it. Their analysis will clarify which library is better and will highlight the requirements for creating adapted graphics. The results obtained from the tests are then studied, proving that it’s possible to make
digital graphics accessible in a pretty simple way. And that users can interact easily with them. Users have referred to a high degree of satisfaction with the adapted graphics.
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Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023, Director: Mireia Ribera i Josep Vives i Santa Eulàlia
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ZABALA AGUILAR, Clara. Estadística freqüentista versus estadística bayesiana: una comparativa amb gràfics d’accessibilitat millorada. [consulta: 15 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/199420]