Rafael B Stern
I am an Assistant Professor at the Federal University of Sao Carlos. I have a B.A. in statistics from University of Sao Paulo, a B.A. in law from Pontificia Universidade Catolica in Sao Paulo, and a Ph.D. in statistics from Carnegie Mellon University. I am currently a member of the Scientific Council of the Brazilian Association of Jurimetrics and an associate investigator at NeuroMat.
less
Related Authors
InterestsView All (11)
Uploads
Papers by Rafael B Stern
The gold standard for finding these relationships is the Comparative Method. Despite the success of the Comparative Method in finding language relationships, it suffers from at least two limitations. First, the Comparative Method involves the manual comparison of various features from a group of languages. Second, the Comparative Method doesn't provide a numerical measure of evidence for how much the database under consideration corroborates an hypothesis.
Given the above limitations, the field of Computational Historical Linguistics is presented as a complement to the Comparative Method. This field has experienced a recent expansion with the adaptation of methods from biological phylogenetics. Nevertheless, there is debate whether the evolutionary models used in phylogenetics also incorporate valid linguistical assumptions.
In this thesis, I propose a new (probability) model for the evolution of the phonology of languages. A relevant innovation of this model is that it captures the regularity of sound changes. I also describe the software that I created in order to assist in incorporating into the model a linguist's expert knowledge. I show that the knowledge obtained in this way agrees with qualitative statements known to linguists. Finally, I present a new algorithm used to compute the probability of linguistic hypotheses regarding language relationships and the occurence of regular sound changes. The main problem that this algorithm overcomes is that it efficiently explores
the possible regular sound changes, mutations in languages that simultaneously affect several words. In order to overcome this challange, I present a new variant of Nested Sequential Monte Carlo that is used to explore the large space of language relationships and regular sound changes. To the best of my knowledge, this is the first algorithm that can perform joint inference on regular sound changes and evolutionary trees. I show that this algorithm is a special case of Sequential Importance Resampling.
The gold standard for finding these relationships is the Comparative Method. Despite the success of the Comparative Method in finding language relationships, it suffers from at least two limitations. First, the Comparative Method involves the manual comparison of various features from a group of languages. Second, the Comparative Method doesn't provide a numerical measure of evidence for how much the database under consideration corroborates an hypothesis.
Given the above limitations, the field of Computational Historical Linguistics is presented as a complement to the Comparative Method. This field has experienced a recent expansion with the adaptation of methods from biological phylogenetics. Nevertheless, there is debate whether the evolutionary models used in phylogenetics also incorporate valid linguistical assumptions.
In this thesis, I propose a new (probability) model for the evolution of the phonology of languages. A relevant innovation of this model is that it captures the regularity of sound changes. I also describe the software that I created in order to assist in incorporating into the model a linguist's expert knowledge. I show that the knowledge obtained in this way agrees with qualitative statements known to linguists. Finally, I present a new algorithm used to compute the probability of linguistic hypotheses regarding language relationships and the occurence of regular sound changes. The main problem that this algorithm overcomes is that it efficiently explores
the possible regular sound changes, mutations in languages that simultaneously affect several words. In order to overcome this challange, I present a new variant of Nested Sequential Monte Carlo that is used to explore the large space of language relationships and regular sound changes. To the best of my knowledge, this is the first algorithm that can perform joint inference on regular sound changes and evolutionary trees. I show that this algorithm is a special case of Sequential Importance Resampling.