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1	L'institution harmonique (ca. 1640-1647) de Charles Guillet / Institution harmonique (ca. 1640-1647) of Charles Guillet Grimaldi, Amarine 08 March 2013 (has links) Cette thèse porte sur l’Institution harmonique, un traité manuscrit composé par Charles Guillet entre 1640 et 1647, dédié à l’archiduc Léopold-Guillaume. Une étude introductoire précède la transcription de la dédicace, de la préface et du premier livre (le seul qui subsiste). Elle met en lumière l’auteur Charles Guillet (ca. 1575-1654), une figure originale mais peu connue dans le paysage musical et apporte des éclairages sur la source manuscrite (notamment organisation, contenu et dessein théorique). La construction de son discours est enfin analysée à travers l’usage des sources (choix des autorités et compilation de deux « phares harmoniques » qu’étaient Zarlino et Salinas). Par la mise en scène de controverses, Guillet démontre la supériorité de la division syntone sur la diatone et justifie la théorie modale de Zarlino. Dans le premier livre sur « la Theorie, ou Speculative Musicale », la théorie arithmétique des rapports et des proportions est appliquée aux intervalles puis aux questions pratiques du tempérament / This dissertation deals with the Institution Harmonique, a hand written treatise, composed by Charles Guillet between 1640 and 1647, dedicated to Archduke Leopold Wilhelm. An introductory study precedes the transcription of the dedication, preface and Part 1 (the only one remaining to this day). It introduces author Charles Guillet (ca. 1575-1654), an original yet poorly known figure of the musical scene. You will find some specifications regarding the hand-written source (organisation, content and theoretic purpose). I will analyse the construction of discourse through the use of the various sources (choice of authorities and compilation of Zarlino and Salinas, two « harmonic lighthouses ». By staging of controversies, Guillet demonstrates the superiority of syntonic tuning on Pythagorean tuning and justifies the zarlinian modal theory. In the Part 1 on « The Theory, or musical speculative », the arithmetic theory of ratios and proportions is applied to the intervals then to the practical questions regarding temperament Théorie musicale en langue française Première moitié du XVIIe siècle Bruges Fantaisies Orgue Controverses Son Proportion Intervalle Genre Mode Polycorde Tempérament Salinas Zarlino Music theory in French First half of the Seventeenth Century Bruges Fantasies Organ music Controversy Sound Proportion Interval Genera Mode Polychord Temperament Salinas Zarlino
2	Bayesian methods and machine learning in astrophysics Higson, Edward John January 2019 (has links) This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and background material in Chapters 1 and 2, Chapter 3 analyses the sampling errors in nested sampling parameter estimation and presents a method for estimating them numerically for a single nested sampling calculation. Chapter 4 introduces diagnostic tests for detecting when software has not performed the nested sampling algorithm accurately, for example due to missing a mode in a multimodal posterior. The uncertainty estimates and diagnostics in Chapters 3 and 4 are implemented in the $\texttt{nestcheck}$ software package, and both chapters describe an astronomical application of the techniques introduced. Chapter 5 describes dynamic nested sampling: a generalisation of the nested sampling algorithm which can produce large improvements in computational efficiency compared to standard nested sampling. We have implemented dynamic nested sampling in the $\texttt{dyPolyChord}$ and $\texttt{perfectns}$ software packages. Chapter 6 presents a principled Bayesian framework for signal reconstruction, in which the signal is modelled by basis functions whose number (and form, if required) is determined by the data themselves. This approach is based on a Bayesian interpretation of conventional sparse reconstruction and regularisation techniques, in which sparsity is imposed through priors via Bayesian model selection. We demonstrate our method for noisy 1- and 2-dimensional signals, including examples of processing astronomical images. The numerical implementation uses dynamic nested sampling, and uncertainties are calculated using the methods introduced in Chapters 3 and 4. Chapter 7 applies our Bayesian sparse reconstruction framework to artificial neural networks, where it allows the optimum network architecture to be determined by treating the number of nodes and hidden layers as parameters. We conclude by suggesting possible areas of future research in Chapter 8.
3	Computational Bayesian techniques applied to cosmology Hee, Sonke January 2018 (has links) This thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {< } -1$, but at low statistical significance. The constraining power of various datasets is quantified, finding that data constraints peak around redshift $z = 0.2$ due to baryonic acoustic oscillation and supernovae data constraints, whilst cosmic microwave background radiation and Lyman-$\alpha$ forest constraints are less significant. Specific models with a conformal time symmetry in the Friedmann equation and with an additional dark energy component are tested and shown to be competitive to the vacuum dark energy model by Bayesian model selection analysis: that they are not ruled out is believed to be largely due to poor data quality for deciding between existing models. Recent detections of gravitational waves by the LIGO collaboration enable the first gravitational wave tests of general relativity. An existing test in the literature is used and sped up significantly by a novel method developed in this thesis. The test computes posterior odds ratios, and the new method is shown to compute these accurately and efficiently. Compared to computing evidences, the method presented provides an approximate 100 times reduction in the number of likelihood calculations required to compute evidences at a given accuracy. Further testing may identify a significant advance in Bayesian model selection using nested sampling, as the method is completely general and straightforward to implement. We note that efficiency gains are not guaranteed and may be problem specific: further research is needed.

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