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Joannes Tollius (c. 1550-c. 1620) was born in Amersfoort and began his career as music director of the Amersfoort Chapel of Our Lady. He flourished in Italy as maestro di capella of the cathedrals of Rieti (1583-84) and Assisi (1584-86), and as cantor tenorista in Rome (1586-88) and Padua (1588-1601). He ended his career as an exceptionally well-paid singer in the court chapel of Christian IV in Copenhagen (1601-03). In 1590, a collection of three-part motets appeared under the surprising title Motecta de dignitate et moribus sacerdotum (motets about the dignity and morals of priests), presumably intended as a denunciation of the priests who had had him imprisoned in Assisi on charge of heresy, a charge of which Tollius was later acquitted. In 1591, two collections of five-part motets appeared in quick succession. In these motets, Tollius uses techniques of word painting that go significantly further than those used by his contemporaries in sacred music. In 1597, an extended and revised reprint appeared of the a collection of six-part madrigals. In his madrigals, Tollius shows himself to be a skilled composer who is in keeping with the madrigal output of his Italian contemporaries. In 1598, two new madrigals by Tollius appeared in collections of works by Paduan masters, including such great names as Lodovico Viadana and Costanzo Porta. Tollius is unconventional in his compositions. He combines works with an archaic character, with works that fit in prevailing compositional trends, but he also experiments with means that go far beyond what his contemporaries allowed themselves. His oeuvre may be small, but its diversity and quality makes it notable. During his life, Tollius regularly came into conflict with his employers. He was often fined and sometimes even imprisoned. Contemporary accounts of Tollius are contradictory when it comes to his personality, but they are unanimous in recognising that he was a skilled musician.
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
In the Introduction, a brief general review is given of the present knowledge and ideas about the Hunebed Builders, who lived some 5000 years ago during the Stone Age.