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Simola, Umberto (2018) Developments in Approximate Bayesian Computation and Statistical Applications in Astrostatistics. [Ph.D. thesis]

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Abstract (italian or english)

The title of this Thesis embraces two topics that have been investigated. Most of the present work is dedicated to develops and extensions for Approximate Bayesian Computation (ABC). While several algorithms have been proposed to improve the efficiency of the basic ABC algorithm, a number of subjective choices is left to the researcher. Several of these choices have not only an impact on the efficiency of the algorithm but also on its capability to approximate properly the true posterior distribution. We present a first extension of the ABC Population Monte-Carlo (ABC-PMC) algorithm aimed by the goal of minimizing the number of subjective inputs required to the user, improving at the same time the computational efficiency of the algorithm. In the second work we propose extensions of the ABC-PMC algorithm as an alternative framework for inference to work with finite mixture models. The second topic was initiated from a collaboration between the Statistics and Data Science Department and the Astronomy Department at Yale University and the Department of Physics at the University of Geneve, with the goal of detecting and characterizing "Earth-like" extrasolar planets. We propose a novel statistical tool to better disentangle stellar activity from the pure signal coming from an extrasolar planet, aimed by the goal of detecting and characterizing "Earth-like'' planets.

Abstract (a different language)

Il titolo di questa Tesi vuole abbracciare i due differenti argomenti che sono stati investigati. La maggior parte del presente lavoro é dedicata a sviluppi ed estensioni dell' algoritmo Approximate Bayesian Computation Population Monte-Carlo (ABC-PMC). Mentre parecchi algoritmi sono stati proposti per migliorare l'efficienza della procedura base ABC, alcune scelte soggettive vengono lasciate al ricercatore. Alcune di queste scelte hanno non solo un impatto sull'efficienza dell'algoritmo, ma anche sulla capacità del medesimo di approssimare in maniera consona la vera distribuzione a posteriori. Noi presentiamo una prima estensione dell'algoritmo ABC-PMC che vuole minimizzare il numero di scelte soggettive richieste all'utente, con l'obiettivo di migliorare l'efficienza dell'algoritmo preservando al contempo l'ottenimento di una fedele approssimazione della vera distribuzione a posteriori. Come seconda estensione, proponiamo una procedura basata sull'algoritmo ABC-PMC per lavorare con modelli mistura (caso finito). Il secondo argomento descrive uno dei risultati della collaborazione tra il Dipartimento di Astronomia e il Dipartimento di Statistica e Data Science all' Università di Yale ed il Dipartimento di Fisica all'Università di Ginevra, dove l'obiettivo consiste nello scovare e caratterizzare pianeti extrasolari. Noi proponiamo una nuova tecnica statistica per meglio separare l'attività stellare dal puro segnale proveniente da un pianeta extrasolare, con l'obiettivo di scovare e caratterizzare esopianeti terrestri teoricamente adatti ad ospitare la vita.

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EPrint type:Ph.D. thesis
Tutor:Brazzale, Alessandra Rosalba and Cisewski, Jessica
Supervisor:Brazzale, Alessandra Rosalba and Cisewski, Jessica
Ph.D. course:Ciclo 30 > Corsi 30 > SCIENZE STATISTICHE
Data di deposito della tesi:29 June 2018
Anno di Pubblicazione:06 June 2018
Key Words:Approximate Bayesian Computation Finite Mixture Models Astrostatistics Exoplanets
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/02 Statistica per la ricerca sperimentale e tecnologica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:11267
Depositato il:06 Nov 2019 12:42
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