Search

Your search keyword '"Department of Statistics [Vancouver] (UBC Statistics)"' showing total 6 results
Start Over Author "Department of Statistics [Vancouver] (UBC Statistics)" Language english
6 results on '"Department of Statistics [Vancouver] (UBC Statistics)"'

1. A review of species role concepts in food webs

Author

Giulio Valentino Dalla Riva, Marília P. Gaiarsa, E. Fernando Cagua, Alyssa R. Cirtwill, Camille Coux, Malyon D. Bimler, D. Matthias Dehling, Linköping University (LIU), Department of Statistics [Vancouver] (UBC Statistics), University of British Columbia (UBC), University of Canterbury [Christchurch], Universidade de São Paulo (USP), Department of Entomology [Riverside], University of California [Riverside] (UCR), University of California-University of California, School of Biological Sciences, University of Queensland [Brisbane], Santé et agroécologie du vignoble (UMR SAVE), and Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)

Subjects
0106 biological sciences, Ecological niche, Ekologi, eltonian niche, Ecology, 010604 marine biology & hydrobiology, media_common.quotation_subject, [SDV]Life Sciences [q-bio], Eltonian niche, network structure, Ambiguity, 010603 evolutionary biology, 01 natural sciences, Epistemology, Role Concepts, [SDE]Environmental Sciences, Sociology, Centrality, Ecology, Evolution, Behavior and Systematics, media_common
Abstract

Many different concepts have been used to describe species' roles in food webs (i.e., the ways in which species participate in their communities as consumers and resources). As each concept focuses on a different aspect of food-web structure, it can be difficult to relate these concepts to each other and to other aspects of ecology. Here we use the Eltonian niche as an overarching framework, within which we summarize several commonly-used role concepts (degree, trophic level, motif roles, and centrality). We focus mainly on the topological versions of these concepts but, where dynamical versions of a role concept exist, we acknowledge these as well. Our aim is to highlight areas of overlap and ambiguity between different role concepts and to describe how these roles can be used to group species according to different strategies (i.e., equivalence and functional roles). The existence of “gray areas” between role concepts make it essential for authors to carefully consider both which role concept(s) are most appropriate for the analyses they wish to conduct and what aspect of species' niches (if any) they wish to address. The ecological meaning of differences between species' roles can change dramatically depending on which role concept(s) are used.

Published
2018
Full Text
View/download PDF

2. Sparsity-Promoting Bayesian Dynamic Linear Models

Author

Caron, François, Bornn, Luke, Doucet, Arnaud, Advanced Learning Evolutionary Algorithms (ALEA), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Department of Statistics [Vancouver] (UBC Statistics), University of British Columbia (UBC), Department of Statistics [Oxford], University of Oxford [Oxford], INRIA, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and University of Oxford

Subjects
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML], dynamic regression, sparsity, particle filters, Gaussian mixture models, particle MCMC, [STAT.CO]Statistics [stat]/Computation [stat.CO], generalized hyperbolic, [STAT.ME]Statistics [stat]/Methodology [stat.ME]
Abstract

Sparsity-promoting priors have become increasingly popular over recent years due to an increased number of regression and classification applications involving a large number of predictors. In time series applications where observations are collected over time, it is often unrealistic to assume that the underlying sparsity pattern is fixed. We propose here an original class of flexible Bayesian linear models for dynamic sparsity modelling. The proposed class of models expands upon the existing Bayesian literature on sparse regression using generalized multivariate hyperbolic distributions. The properties of the models are explored through both analytic results and simulation studies. We demonstrate the model on a financial application where it is shown that it accurately represents the patterns seen in the analysis of stock and derivative data, and is able to detect major events by filtering an artificial portfolio of assets.

Published
2012

3. Fluctuations of Interacting Markov Chain Monte Carlo Models

Author

Bercu, Bernard, Del Moral, Pierre, Doucet, Arnaud, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Advanced Learning Evolutionary Algorithms (ALEA), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Dept of Statistics & Dept of Computer Science, University of British Columbia (UBC), Department of Statistics [Vancouver] (UBC Statistics), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR], 47H20, 60G35, 60J85, 62G09, 47D08, 47G10, 62L20
Abstract

International audience; We present a functional central limit theorem for a general class of interacting Markov chain Monte Carlo interpretations of discrete generation measure-valued equations. The path space models associated with these stochastic processes belong to the class of nonlinear Markov chains interacting with their empirical occupation measures. We develop an original theoretical analysis based on resolvent operators and semigroup techniques to analyze the fluctuation of their occupation measures around their limiting value. We also present a set of simple regularity conditions that applies to interacting Markov chain Monte Carlo models on path spaces, yielding what seems to be the first fluctuation theorems for this class of self-interacting models.

Published
2012

4. On Adaptive Resampling Procedures for Sequential Monte Carlo Methods

Author

Del Moral, Pierre, Doucet, Arnaud, Jasra, Ajay, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Advanced Learning Evolutionary Algorithms (ALEA), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Dept of Statistics & Dept of Computer Science, University of British Columbia (UBC), Department of Statistics [Vancouver] (UBC Statistics), Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR], 65C05, 60G35, 60J85, 62G09, 47D08, 47G10, 62L20
Abstract

International audience; Sequential Monte Carlo (SMC) methods are a general class of techniques to sample approximately from any sequence of probability distributions. These distributions are approximated by a cloud of weighted samples which are propagated over time using a combination of importance sampling and resampling steps. This article is concerned with the convergence analysis of a class of SMC methods where the times at which resampling occurs are computed on-line using criteria such as the effective sample size. This is a popular approach amongst practitioners but there are very few convergence results available for these methods. It is shown here that these SMC algorithms correspond to a particle approximation of a Feynman-Kac flow of measures on adaptive excursion spaces. By combining a non-linear distribution flow analysis to an original coupling technique, we obtain functional central limit theorems and uniform exponential concentration estimates for these algorithms. The original exponential concentration theorems presented in this study significantly improve previous concentration estimates obtained for SMC algorithms.

Published
2012
Full Text
View/download PDF

5. Bayesian clustering in decomposable graphs

Author

Luke Bornn, François Caron, Department of Statistics [Vancouver] (UBC Statistics), University of British Columbia (UBC), Advanced Learning Evolutionary Algorithms (ALEA), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, FOS: Computer and information sciences, Decomposable graphs, media_common.quotation_subject, Bayesian probability, Bayesian analysis, Machine Learning (stat.ML), [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Machine learning, computer.software_genre, 01 natural sciences, Statistics - Applications, Clustering, Methodology (stat.ME), 010104 statistics & probability, Statistics - Machine Learning, Voting, 0502 economics and business, Prior probability, Applications (stat.AP), Graphical model, 0101 mathematics, Cluster analysis, Statistics - Methodology, 050205 econometrics, media_common, Mathematics, [STAT.AP]Statistics [stat]/Applications [stat.AP], Product partition models, business.industry, American voting, Applied Mathematics, 05 social sciences, Agriculture, Partition (database), Graph, Bayesian clustering, Artificial intelligence, business, computer, [STAT.ME]Statistics [stat]/Methodology [stat.ME]
Abstract

In this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edges, the proposed work empowers practitioners to control clustering, level of separation, and other features of the graph. Emphasis is placed on a particular prior distribution which derives its motivation from the class of product partition models; the properties of this prior relative to existing priors is examined through theory and simulation. We then demonstrate the use of graphical models in the field of agriculture, showing how the proposed prior distribution alleviates the inflexibility of previous approaches in properly modeling the interactions between the yield of different crop varieties., 3 figures, 1 table

Published
2010
Full Text
View/download PDF

6. A Functional Central Limit Theorem for a Class of Interacting Markov Chain Monte Carlo Models

Author

Bercu, Bernard, Del Moral, Pierre, Doucet, Arnaud, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Advanced Learning Evolutionary Algorithms (ALEA), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Dept of Statistics & Dept of Computer Science, University of British Columbia (UBC), Department of Statistics [Vancouver] (UBC Statistics), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR], martingale limit theorems, random fields, Markov chain Monte Carlo models, 47H20, 60G35, 60J85, 62G09, 47D08, 47G10, 62L20, Multivariate and functional central limit theorems, and Feynman-Kac integrals, self-interacting Markov chains
Abstract

International audience; We present a functional central limit theorem for a new class of interaction Markov chain Monte Carlo interpretations of discrete generation measure valued equations. We provide an original stochastic analysis based on semigroup techniques on distribution spaces and fluctuation theorems for self-interaction random fields. Besides the fluctuation analysis of these models, we also present a series of sharp $\LL_m$-mean error bounds in terms of the semigroup associated with the first order expansion of the limiting measure valued process, yielding what seems to be the first results of this type for this class of interacting processes. We illustrate these results in the context of Feynman-Kac integration semigroups arising in physics, biology and stochastic engineering science.

Published
2009

Catalog

Books, media, physical & digital resources
See catalog results