Exact Bayesian inference for off-line change-point detection in tree-structured graphical models

Schwaller, Loïc; Robin, Stéphane

doi:10.1007/s11222-016-9689-3

Cited by 13 publications

(13 citation statements)

References 19 publications

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“…Our method achieves generally a greater area under the PR curve than others. The present results also confirm that obtained by Schwaller et al (), namely, the relative good performance of tree‐structured graphical models compared to sampling‐based approaches despite stronger restrictions on the class of graphical models. However, the performance of the approach can degrade in some cases (eg, cluster structures).…”

Section: Numerical Experimentssupporting

confidence: 91%

“…We compare our method to two sampling‐based approaches based on the birth‐death and reversible jump Markov chain Monte Carlo (MCMC) algorithms, developed by Mohammadi and Wit (; ), using 100 000 sweeps and a burn‐in period of 50 000 updates. We also consider the method of Schwaller et al () that offers closed‐form inference within the class of tree‐structured graphical models. For each method we obtain the marginal posterior probabilities of edge inclusion, either via the sampling algorithm or exactly.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Fast Bayesian Inference in Large Gaussian Graphical Models

Leday

Richardson

2019

Biometrics

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Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and conditional independence structures between variables by multiple testing, which bypasses the exploration of the model space. Specifically, we introduce closed-form Bayes factors under the Gaussian conjugate model to evaluate the null hypotheses of marginal and conditional independence between variables.Their computation for all pairs of variables is shown to be extremely efficient, thereby allowing us to address large problems with thousands of nodes as required by modern applications. Moreover, we derive exact tail probabilities from the null distributions of the Bayes factors. These allow the use of any multiplicity correction procedure to control error rates for incorrect edge inclusion. We demonstrate the proposed approach on various simulated examples as well as on a large gene expression data set from The Cancer Genome Atlas. K E Y W O R D S

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Section: Numerical Experimentssupporting

confidence: 91%

Section: Numerical Experimentsmentioning

confidence: 99%

Fast Bayesian Inference in Large Gaussian Graphical Models

Leday

Richardson

2019

Biometrics

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“…Networks comparison is a wide and interesting question and tools lack to check which edges are shared by a set of networks. The approach introduced by Schwaller and Robin (2017) could be adapted to EMtree framework. Lastly, it is also very likely that not all covariates nor even all species have been measured or observed.…”

Section: Discussionmentioning

confidence: 99%

Tree‐based inference of species interaction networks from abundance data

2020

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1. The behavior of ecological systems mainly relies on the interactions between the species it involves. In many situations, these interactions are not observed and have to be inferred from species abundance data. To be relevant, any reconstruction network methodology needs to handle count data and to account for possible environmental effects. It also needs to distinguish between direct and indirect interactions and graphical models provide a convenient framework for this purpose. 2. We introduce a generic statistical model for network reconstruction based on abundance data. The model includes fixed effects to account for environmental covariates and sampling efforts, and correlated random effects to encode species interactions. The inferred network is obtained by averaging over all possible tree-shaped (and therefore sparse) networks, in a computationally efficient manner. An output of the procedure is the probability for each edge to be part of the underlying network. 3. A simulation study shows that the proposed methodology compares well with state-of-theart approaches, even when the underlying network strongly differs from a tree. The analysis of two data sets highlights the influence of covariates on the inferred network. 4. Accounting for covariates is critical to avoid spurious edges. The proposed approach could be extended to perform network comparison or to look for missing species.

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“…This is not the case in our approach, where we are explicitly imposing a continuity assumption between segments which allows us to detect changes in slope. With the exception of Dobigeon et al (2007); Schwaller and Robin (2017), all other methods are focusing on univariate time-series.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling

Papastamoulis

Furukawa

Rhijn

et al. 2019

The International Journal of Biostatistics

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We consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. We develop a Bayesian approach to infer the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of changepoints gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) sampler is constructed for approximating the posterior distribution. Our method is benchmarked using simulated data and is applied to uncover differences in the dynamics of fungal growth from imaging time course data collected from different strains. The source code is available on CRAN.

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Exact Bayesian inference for off-line change-point detection in tree-structured graphical models

Cited by 13 publications

References 19 publications

Fast Bayesian Inference in Large Gaussian Graphical Models

Fast Bayesian Inference in Large Gaussian Graphical Models

Tree‐based inference of species interaction networks from abundance data

Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling

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