Nonparametric belief propagation

Sudderth, Erik B.; Ihler, Alexander T.; Isard, Michael; Freeman, William T.; Willsky, Alan S.

doi:10.1145/1831407.1831431

Cited by 232 publications

(329 citation statements)

References 35 publications

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“…However, a different fate at an internal node would yield a different expectation for the gene expression of the duplicates. We therefore modify the Nonparametric Belief Propagation (NBP) [18] to infer ancestral gene expression levels. NBP allows us to incorporate evolutionary fates (SF, CF, NF) at each internal node to infer ancestral gene expression level.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we repeat forward and backward particle passing procedures using M particles drawn from the re-estimated GMMs. The parameters of GMM will converge after several NBP iterations T [18], where T is often around 500 ~ 1000 in practice.…”

Section: Methodsmentioning

confidence: 99%

“…A key step in our proposed approach is to predict the ancestral gene expression level based on the phylogenetic tree of the paralogs and known expression levels at leaves. We modify the nonparametric belief propagation (NBP) [18] algorithm for this task. NBP is a graphical model-based tracking algorithm widely used in human motion tracking problems such as the gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

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Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation

Zeng

Hannenhalli

2013

BMC Genomics

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BackgroundGene duplication, followed by functional evolution of duplicate genes, is a primary engine of evolutionary innovation. In turn, gene expression evolution is a critical component of overall functional evolution of paralogs. Inferring evolutionary history of gene expression among paralogs is therefore a problem of considerable interest. It also represents significant challenges. The standard approaches of evolutionary reconstruction assume that at an internal node of the duplication tree, the two duplicates evolve independently. However, because of various selection pressures functional evolution of the two paralogs may be coupled. The coupling of paralog evolution corresponds to three major fates of gene duplicates: subfunctionalization (SF), conserved function (CF) or neofunctionalization (NF). Quantitative analysis of these fates is of great interest and clearly influences evolutionary inference of expression. These two interrelated problems of inferring gene expression and evolutionary fates of gene duplicates have not been studied together previously and motivate the present study.ResultsHere we propose a novel probabilistic framework and algorithm to simultaneously infer (i) ancestral gene expression and (ii) the likely fate (SF, NF, CF) at each duplication event during the evolution of gene family. Using tissue-specific gene expression data, we develop a nonparametric belief propagation (NBP) algorithm to predict the ancestral expression level as a proxy for function, and describe a novel probabilistic model that relates the predicted and known expression levels to the possible evolutionary fates. We validate our model using simulation and then apply it to a genome-wide set of gene duplicates in human.ConclusionsOur results suggest that SF tends to be more frequent at the earlier stage of gene family expansion, while NF occurs more frequently later on.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation

Zeng

Hannenhalli

2013

BMC Genomics

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“…This can be done using any applicable inference mechanism. We use nonparametric belief propagation [21] optimized to exploit the specific structure of this inference problem [6]. The particles used to represent the densities are directly derived from individual feature observations.…”

Section: Markov Network For Object Representationmentioning

confidence: 99%

Learning Visual Representations for Interactive Systems

Piater

Jodogne

Detry

et al. 2011

Springer Tracts in Advanced Robotics

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We describe two quite different methods for associating action parameters to visual percepts. Our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classification algorithm that seeks to split perceptual states so as to reduce perceptual aliasing. This results in an adaptive discretization of the perceptual space based on the presence or absence of visual features. Its extension RLJC also handles continuous action spaces. In contrast to the minimalistic visual representations produced by RLVC and RLJC, our second method learns structural object models for robust object detection and pose estimation by probabilistic inference. To these models, the method associates grasp experiences autonomously learned by trial and error. These experiences form a nonparametric representation of grasp success likelihoods over gripper poses, which we call a grasp density. Thus, object detection in a novel scene simultaneously produces suitable grasping options.

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“…We solve them in a unified manner by belief propagation (BP) [21] [22]. Augmenting nodes are introduced as nodes that do not correspond to any specific object but are responsible for generating new object nodes by receiving belief messages from nodes of key objects.…”

Section: Integration Of Observation and Spatio-temporal Contextmentioning

confidence: 99%

Key Object Driven Multi-category Object Recognition, Localization and Tracking Using Spatio-temporal Context

Nevatia

2008

Lecture Notes in Computer Science

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Abstract. In this paper we address the problem of recognizing, localizing and tracking multiple objects of different categories in meeting room videos. Difficulties such as lack of detail and multi-object co-occurrence make it hard to directly apply traditional object recognition methods. Under such circumstances, we show that incorporating object-level spatio-temporal relationships can lead to significant improvements in inference of object category and state. Contextual relationships are modeled by a dynamic Markov random field, in which recognition, localization and tracking are done simultaneously. Further, we define human as the key object of the scene, which can be detected relatively robustly and therefore is used to guide the inference of other objects. Experiments are done on the CHIL meeting video corpus. Performance is evaluated in terms of object detection and false alarm rates, object recognition confusion matrix and pixel-level accuracy of object segmentation.

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Nonparametric belief propagation

Cited by 232 publications

References 35 publications

Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation

Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation

Learning Visual Representations for Interactive Systems

Key Object Driven Multi-category Object Recognition, Localization and Tracking Using Spatio-temporal Context

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