1,730 research outputs found
Resampling from the past to improve on MCMC algorithms
No full textWe introduce the idea that resampling from past observations in a Markov Chain Monte Carlo sampler can fasten convergence. We prove that proper resampling from the past does not disturb the limit distribution of the algorithm. We illustrate the method with two examples. The first on a Bayesian analysis of stochastic volatility models and the other on Bayesian phylogeny reconstruction.Monte Carlo methods, Resampling, Stochastic volatility models, Bayesian phylogeny reconstruction.
Topics in sparse Bayesian machine learning
No full text2023This dissertation is devoted to addressing several challenging problems in machine learning via the Bayesian approach. One popular approach to Bayesian deep learning is to use Monte Carlo methods, such as Markov Chain Monte Carlo (MCMC), to approximate the posterior distribution. These methods generate a set of samples from the posterior, which can be used to quantify the uncertainty in the parameters and make probabilistic predictions. Bayesian methods in deep learning provide a framework for incorporating uncertainty into the learning process and can lead to more robust models with improved performance on unseen data. They have been applied to a wide range of problems, including image classification, reinforcement learning, and generative models, among others. This dissertation is organized as follows. First chapter is fast asynchronous sampler in sparse bayesian learning. In this chapter, We propose a very fast approximate Markov Chain Monte Carlo(MCMC) sampling framework that is applicable to a large class of sparse Bayesian inference problems, where the computational cost per iteration in several regression models is of order O(n(s + J)), where n is the sample size, s the underlying sparsity of the model, and J is the size of a randomly selected subset of regressors. This cost can be further reduced by data sub-sampling when stochastic gradient Langevin dynamics are employed. The algorithm is an extension of the asynchronous Gibbs sampler of Johnson et al. (2013), but can be viewed from a statistical perspective as a form of Bayesian iterated sure independent screening (Fan et al. (2009)). We show that in high-dimensional linear regression problems, the Markov chain generated by the proposed algorithm admits an invariant distribution that recovers correctly the main signal with high probability under some statistical assumptions. Furthermore we show that its mixing time is at most linear in the number of regressors. We illustrate the algorithm with several models. Second chapter is A one-step Laplace Approximation for high-dimensional variable selection. In this chapter, we introduce a rapid one-step Laplace approximation method, referred to as OLAP, which effectively tackles the computational burden of variable selection in high dimensions. Our findings demonstrate that this approximation offers a consistent variable selection procedure under reasonable assumptions. Additionally, we establish that the mixing time of the Gibbs sampler, employed for sampling from the posterior distribution of OLAP, scales linearly with the dimension p. Through comprehensive simulations, we validate the efficiency and accuracy of our proposed sampler, highlighting its potential to significantly enhance variable selection processes. Third chapter is Sparse(Cyclical) MCMC in Deep Neural Networks. In this chapter, we propose a general cyclical MCMC framework for a class of Bayesian inference problem, aiming to generate samples from one single mode in each cycle andhave mode swapping among different cycles to capture multimodality. We provide extensive results on the performance of prediction, multimodality of different cyclical MCMC methods on high-dimensional gaussian mixture models. We then introduce the sparse cyclical MCMC sampler in deep neural networks and present promising simulation results from the perspective of uncertainty estimation and calibration
Yves-Heng Lim
No full textYves-Heng Lim est enseignant-chercheur au Département d’Etudes de Sécurité et de Criminologie de l’Université Macquarie, Sydney. Il est l’auteur de China’s Naval Power: An Offensive Realist Approach (Ashgate, 2014). Yves-Heng Lim is a lecturer at the Department of Security Studies and Criminology, Macquarie University. He is the author of China’s Naval Power: An Offensive Realist Approach (Ashgate, 2014)
The question of empire today
No full textY.C. Zarka, tomando como referencia la política exterior de Estados Unidos, presenta una reflexión en perspectiva histórica, política y filosófica sobre la idea contemporánea de imperio. Los modelos del imperio romano y del imperio colonial han sido abandonados. El imperialismo contemporáneo impone su hegemonía por las vías económica y cultural. Partiendo de la dialéctica imperio-imperialismo, desde los conceptos de soberanía y democracia, el autor muestra las contradicciones internas y externas de lo que denomina repúblicas imperiales. Desde la primera perspectiva, la hegemonía imperial puede ser interpretada al mismo tiempo como la expresión y la crisis de la soberanía. Desde la segunda, la contradicción se encuentra en la justificación de una política internacional intervencionista en nombre de las ideas de libertad, república y democracia.Y.C. Zarka, has taking the foreign policy of the United States as a reference, presenting a reflection on the contemporary idea of empire in historical, political and philosophical perspective. The models of the Roman Empire and the colonial empire have been left behind. The contemporary imperialism enforces its hegemony by economy and culture. Due the empire-imperialism dialectic, the author shows the internal and external contradictions of what he names "imperial republics". For instance: the imperial hegemony can be interpreted as expression and crisis of the idea sovereignty; the interventionist international policy is justified on behalf of freedom, republic and democracy ideas.Publicad
Topics in sparse Bayesian machine learning
No full text2023This dissertation is devoted to addressing several challenging problems in machine learning via the Bayesian approach. These problems frequently arise in diverse fields, such as epidemiology, biomedicine, robust statistics and imaging science, and are usually high-dimensional and have certain sparsity assumptions. In this dissertation, we will focus on three important problems, which are sparse canonical correlation analysis, minimum distance estimation and inverse problems. For each problem, we will develop a new method from the Bayesian perspective to solve it effectively and efficiently, with statistical guarantees and numerical evidence
Presentazione dell’edizione italiana di storia della Sicilia da Odisseo ai giorni nostri di Jean-Yves Frétigné
No full textPublished for the first time in 2009, for the Fayard types, and re-edited in 2018 in a "pocket" format, this History of Sicily by Jean-Yves Frétigné, with the subtitle from Odysseus to the present day, presented to the French public as an easy consultation, a "Compagnon de voyage" destined to have an excellent editorial success with over 8,000 copies sold.The Italian translation presented here does not have the presumption of concurring with the monumental works of the history of Sicily which constitute an essential reference by the same Author, as the rich bibliography in the appendix to the volume clearly testifies. Rather, it intends to offer, in just over 400 pages, the “external” look on Sicily of a French historian, as Finley, Mack Smith, Duggan or Norwich have done in the past
Comparison of the thickness of the calvarium between young grey (Halichoerus grypus) and harp (Pagophilus groenlandicus) seals = Comparaison de l'épaisseur de la calotte crânienne entre les jeunes phoques gris (Halichoerus grypus) et les jeunes phoques du Groenland (Pagophilus groenlandicus)
No full textCharles Caraguel, Pierre-Yves Daoust and Fiep de Bie.; 1 online resource (ii, 6 pages); 1919-5044; Distributed by the Government of Canada Depository Services Program (Weekly checklist 2013-35).; Includes bibliographical references.; Mode of access: Internet.; Issued by: Fisheries and Oceans, Quebec Region.; Co-author Pierre-Yves Daoust is a member of faculty at University of Prince Edward Island.; Research document (Canadian Science Advisory Secretariat : Online) ; 2012/172
Интернационализация предприятия путем франшизы на примере Yves Rocher
No full textArtykuł ukazuje franczyzę jako formę internacjonalizacji przedsiębiorstw na przykładzie francuskiego przedsiębiorstwa kosmetycznego Yves Rocher. W części teoretycznej artykułu przedstawiono formy i rodzaje internacjonalizacji przedsiębiorstw oraz scharakteryzowano podstawowe założenia systemu franczyzowego. Główną formą wypowiedzi zastosowaną w tej części artykułu jest streszczenie. Jest to forma wypowiedzi zapewniająca literalny przekaz podstawowych treści zawartych w tekstach źródłowych. W części empirycznej pracy dokonana została syntetyczna analiza koncernu Yves Rocher, wynikająca z toku prowadzonych rozważań teoretycznych dotyczących systemu franczyzowego.The article discusses franchising as a method of internationalization. Based on the theory, the author shows a practical approach to franchising through a real case study. The theoretical part touches on different forms of internationalization and also on basic foundation of franchise system. In the empirical part the author briefly characterizes the Yves Rocher company and their influence on the franchise system.Статья показывает франшизу как форму интернационализации предприятий на примере французского косметического предриятия Yves Rocher. В теоретичесой
части статьи представлены формы и виды интернационализации предприятий, а также охарактеризованы основные положения франшизной системы. Главной
формой высказывания, примененной в этой части статьи, является изложение. Это форма описания, обеспечивающая точную передачу мыслей, содержащихся в первоисточниках. В эмпирической части работы проводится синтетический анализ концерна Yves Rocher, вытекаюший из хода проводимых теоретических рассуждений, касающихся франшизной системы
Yves Bergeron : Une carrière dévolue à comprendre les forêts naturelles et à influencer l’aménagement forestier. Yves Bergeron: A career devoted to understanding natural forests and influencing forest management.
No full textL’article présente une rétrospective de la carrière de Yves Bergeron en faisant le lien avec l’évolution des concepts en écologie forestière et le développement des nouvelles approches en aménagement forestier. Il est construit autour de plusieurs encarts présentant les contributions les plus importantes. Celles-ci sont le développement du cadre écologique comme base de décision pour l’aménagement forestier; la reconstitution historique des feux dans les milieux boréaux; la dynamique forestière après les perturbations naturelles; le développement de l’aménagement écosystémique des milieux forestiers; les enjeux liés à la productivité et aux forêts anciennes et finalement l’impact des changements climatiques. On conclut sur la conviction qu’une transition assistée s’appuyant sur notre connaissance des mécanismes naturels demeure possible bien que le temps presse.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author
An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift
No full textThis paper proposes an adaptive version for the Metropolis adjusted Langevin algorithm with a truncated drift (T-MALA). The scale parameter and the covariance matrix of the proposal kernel of the algorithm are simultaneously and recursively updated in order to reach the optimal acceptance rate of 0:574 (see Roberts and Rosenthal (2001)) and to estimate and use the correlation structure of the target distribution. We develop some convergence results for the algorithm. A simulation example is presented.Markov Chain Monte Carlo, Stochastic approximation algorithms, Metropolis Adjusted Langevin algorithm, geometric rate of convergence.
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