Bayesian computation for statistical models with intractable normalizing constants

This paper deals with some computational aspects in the Bayesian analysis of statistical models with intractable normalizing constants. In the presence of intractable normalizing constants in the likelihood function, traditional MCMC methods cannot be applied. We propose an approach to sample from such posterior distributions. The method can be thought as a Bayesian version of the MCMC-MLE approach of Geyer and Thompson (1992). To the best of our knowledge, this is the first general and asymptotically consistent Monte Carlo method for such problems. We illustrate the method with examples from image segmentation and social network modeling. We study as well the asymptotic behavior of the algorithm and obtain a strong law of large numbers for empirical averages.Comment: 20 pages, 4 figures, submitted for publicatio

Retentional Syntagmatic Network, and its Use in Motivic Analysis of Maqam Improvisation

In this paper is defined a concept of Retentional Syntagmatic Network (RSN), which models the connectivity between temporally closed notes. The RSN formalizes the Schenkerian notion of pitch prolongation as a concept of syntagmatic retention, whose characteristics are dependent on the underlying modal context. This framework enables to formalize the syntagmatic role of ornamentation, and allows an automation of motivic analysis that takes into account melodic transformations. The model is applied to the analysis of a maqam improvisation. The RSN is also proposed as a way to surpass strict hierarchical segmentation models, which in our view cannot sufficiently describe the richness of musical structure. Instead of separability, we propose to focus instead on the connectivity between notes, modeled with the help of RSNs

Suppression of long-branch attraction artefacts in the animal phylogeny using a site-heterogeneous model

BACKGROUND: Thanks to the large amount of signal contained in genome-wide sequence alignments, phylogenomic analyses are converging towards highly supported trees. However, high statistical support does not imply that the tree is accurate. Systematic errors, such as the Long Branch Attraction (LBA) artefact, can be misleading, in particular when the taxon sampling is poor, or the outgroup is distant. In an otherwise consistent probabilistic framework, systematic errors in genome-wide analyses can be traced back to model mis-specification problems, which suggests that better models of sequence evolution should be devised, that would be more robust to tree reconstruction artefacts, even under the most challenging conditions. METHODS: We focus on a well characterized LBA artefact analyzed in a previous phylogenomic study of the metazoan tree, in which two fast-evolving animal phyla, nematodes and platyhelminths, emerge either at the base of all other Bilateria, or within protostomes, depending on the outgroup. We use this artefactual result as a case study for comparing the robustness of two alternative models: a standard, site-homogeneous model, based on an empirical matrix of amino-acid replacement (WAG), and a site-heterogeneous mixture model (CAT). In parallel, we propose a posterior predictive test, allowing one to measure how well a model acknowledges sequence saturation. RESULTS: Adopting a Bayesian framework, we show that the LBA artefact observed under WAG disappears when the site-heterogeneous model CAT is used. Using cross-validation, we further demonstrate that CAT has a better statistical fit than WAG on this data set. Finally, using our statistical goodness-of-fit test, we show that CAT, but not WAG, correctly accounts for the overall level of saturation, and that this is due to a better estimation of site-specific amino-acid preferences. CONCLUSION: The CAT model appears to be more robust than WAG against LBA artefacts, essentially because it correctly anticipates the high probability of convergences and reversions implied by the small effective size of the amino-acid alphabet at each site of the alignment. More generally, our results provide strong evidence that site-specificities in the substitution process need be accounted for in order to obtain more reliable phylogenetic trees

RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.

Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]

Feministisch Flaneren in Film: Van de mannelijke voyeur naar een vrouwelijk flanerend subject in avant-gardefilm van de jaren zestig tot nu.

Degene die kijkt heeft de macht, degene die bekeken wordt is machteloos. Deze dynamiek hoort bij de klassieke cinema die de mannelijke voyeuristische blik produceert en die de vrouw objectiveert en haar bekijkt enkel voor het eigen kijkplezier. Dit is wat ik in het eerste deel (hoofdstuk 1 en 2) van mijn onderzoek blootleg door middel van de klassieke filmtheorie aan de hand van Christian Metz’ theorie van de voyeur en het feministische antwoord op de klassieke filmtheorie van Laura Mulvey. De vraag rijst: Hoe kan de vrouw een actieve subject positie innemen in film? Op zoek naar een alternatieve modus van observatie voor een vrouwelijk subject in film zet ik in deel twee (hoofdstuk 2 en 3) de voyeur af tegen het concept van de flâneur. De flâneur is een historisch, subversief mannelijk figuur dat nauw verbonden is met de moderniteit en het stadse leven, en die het privilege geniet om vrijelijk te kijken en zich door de metropool te bewegen. Binnen het feministische debat zijn er ook vrouwelijke varianten van de flâneur beschreven. Sommige theoretici staan echter kritisch tegenover de vrouwelijke flâneur en achten haar bestaan alleen mogelijk in de vorm van prostituee of consument. Daarentegen zijn er enkelen met mij optimistisch over de mogelijkheid van een vrouwelijke flâneur in film als modus voor een vrouwelijke subjectpositie. De volgende stap in dit onderzoek is het loskoppelen de historische flâneur van de constitutieve eigenschappen, zoals voorgesteld door Ilija Tomanić Trivundža, om de feministische potentie van een vrouwelijke flâneur te onderzoeken. In navolging van Anke Gleber betoog ik dat een vrouwelijk flanerend subject in film zich niet beperkt tot een letterlijk flanerende vrouwelijke protagonist, maar ook ruimte maakt voor figuurlijk flâneren, zoals in de vorm van toeschouwer in de bioscoop, met het oog achter de camera, of als filmregisseur. Aan de hand van een aantal vergelijkende analyses ontwaar ik zowel letterlijk en figuurlijk flanerende vrouwen in avant-gardefilms, zoals in Rape (1969) van Yoko Ono en Cléo de 5 à 7 (1962) van Agnès Varda, vind ik een lesbische flâneur in Portrait d'une jeune fille de la fin des années 60 à Bruxelles (1994) van Chantal Akerman, een queer-flâneur in Night Soil - Economy of Love (2015) van Melanie Bonajo, en eindig mijn onderzoek (hoofdstuk 4) met een gekleurde vrouwelijke flâneur in de Strolling- serie van Cecile Emeke. Met deze casussen toon ik aan dat een vrouwelijk flanerend subject in film inclusief feministische potentie bevat

Interaction features for prediction of perceptual segmentation:Effects of musicianship and experimental task

As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries between structural sections. However, the effects of musical expertise and experimental task on computational modelling of structure are not yet well understood. These issues need to be addressed to better understand how listeners perceive the structure of music and to improve automatic segmentation algorithms. In this study, computational prediction of segmentation by listeners was investigated for six musical stimuli via a real-time task and an annotation (non real-time) task. The proposed approach involved computation of novelty curve interaction features and a prediction model of perceptual segmentation boundary density. We found that, compared to non-musicians’, musicians’ segmentation yielded lower prediction rates, and involved more features for prediction, particularly more interaction features; also non-musicians required a larger time shift for optimal segmentation modelling. Prediction of the annotation task exhibited higher rates, and involved more musical features than for the real-time task; in addition, the real-time task required time shifting of the segmentation data for its optimal modelling. We also found that annotation task models that were weighted according to boundary strength ratings exhibited improvements in segmentation prediction rates and involved more interaction features. In sum, musical training and experimental task seem to have an impact on prediction rates and on musical features involved in novelty-based segmentation models. Musical training is associated with higher presence of schematic knowledge, attention to more dimensions of musical change and more levels of the structural hierarchy, and higher speed of musical structure processing. Real-time segmentation is linked with higher response delays, less levels of structural hierarchy attended and higher data noisiness than annotation segmentation. In addition, boundary strength weighting of density was associated with more emphasis given to stark musical changes and to clearer representation of a hierarchy involving high-dimensional musical changes.peerReviewe