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A nonparametric hierarchical bayesian framework for information filtering

Kai Yu, Volker Tresp, Shipeng Yu
2004 Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04  
This paper suggests that both approaches can be combined under a hierarchical Bayesian framework.  ...  A recent publication [21] made first attempts to unify CF and CBF under a hierarchical Bayesian framework.  ...  In Sec. 2, we will expand on the the idea of modelling information needs of Figure 1 : An illustration of the described hierarchical Bayesian model for information filtering users in a nonparametric hierarchical  ... 
doi:10.1145/1008992.1009053 dblp:conf/sigir/YuTY04 fatcat:o74q2drggfbkbdusx3mmb6qeme

An Introduction to Nonparametric Hierarchical Bayesian Modelling with a Focus on Multi-agent Learning [chapter]

Volker Tresp, Kai Yu
2005 Lecture Notes in Computer Science  
We point out some shortcomings of parametric hierarchical Bayesian modelling and thus focus on a nonparametric approach.  ...  We employ hierarchical Bayesian modelling, which provides a powerful and principled solution.  ...  A Recommendation Engine In this section we provide a summary of the application of nonparametric hierarchical Bayesian modelling to information filtering.  ... 
doi:10.1007/978-3-540-30560-6_13 fatcat:mfokhmftxjexlkrvnhjut7qlri

Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling [chapter]

Maria De Iorio, Stefano Favaro, Yee Whye Teh
2015 Nonparametric Bayesian Inference in Biostatistics  
In this chapter we present a review of some Bayesian parametric and nonparametric models for making inference on population structure, with emphasis on model-based clustering methods.  ...  Our aim is to show how recent developments in Bayesian nonparametrics have been usefully exploited in order to introduce natural nonparametric counterparts of some of the most celebrated parametric approaches  ...  In this chapter we have reviewed model-based clustering methods for population structure within a Bayesian framework.  ... 
doi:10.1007/978-3-319-19518-6_7 fatcat:brjtxeicdzeapptmwxcn5gvali

Bayesian Trend Filtering [article]

Edward A. Roualdes
2015 arXiv   pre-print
We develop a fully Bayesian hierarchical model for trend filtering, itself a new development in nonparametric, univariate regression.  ...  The framework more broadly applies to the generalized lasso, but focus is on Bayesian trend filtering. We compare two shrinkage priors, double exponential and generalized double Pareto.  ...  From this vantage point, we found two distinct bodies of research that offer the framework for a proof of the convergence rate of Bayesian trend filtering.  ... 
arXiv:1505.07710v1 fatcat:ltdu4gxi2zanvbxjs4nyznzdbm

Fast and optimal nonparametric sequential design for astronomical observations [article]

Justin J. Yang, Xufei Wang, Pavlos Protopapas, Luke Bornn
2015 arXiv   pre-print
In this paper, we take a Bayesian model averaging perspective to learn astronomical objects, employing a Bayesian nonparametric approach to accommodate the deviation from convex combinations of known log-SEDs  ...  To effectively use telescope time for observations, we then study Bayesian nonparametric sequential experimental design without conjugacy, in which we use sequential Monte Carlo as an efficient tool to  ...  Design versus inference in the non-conjugate hierarchical Bayesian nonparametric model.  ... 
arXiv:1501.02467v1 fatcat:rchvfimfxna45novoqjmkwjpy4

An Overview of Bayesian Methods for Neural Spike Train Analysis

Zhe Chen
2013 Computational Intelligence and Neuroscience  
Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels.  ...  Some research challenges and opportunities for neural spike train analysis are discussed.  ...  State space analysis and Bayesian filtering offer a principled framework to address these issues [135] .  ... 
doi:10.1155/2013/251905 pmid:24348527 pmcid:PMC3855941 fatcat:nkst6mt3sfcqheuxheda3wq4wq

Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling [article]

Kar Wai Lim, Changyou Chen, Wray Buntine
2016 arXiv   pre-print
Exploiting this additional information, we propose the Twitter-Network (TN) topic model to jointly model the text and the social network in a full Bayesian nonparametric way.  ...  The TN topic model employs the hierarchical Poisson-Dirichlet processes (PDP) for text modeling and a Gaussian process random function model for social network modeling.  ...  Acknowledgement We would like to thank the anonymous reviewers for their helpful feedback and comments.  ... 
arXiv:1609.06791v1 fatcat:bknm22dbgvecrl6puktuycjrmy

Bayesian Nonparametric View to Spawning [article]

Bahman Moraffah
2021 arXiv   pre-print
In this paper, we introduce a novel Bayesian nonparametric approach that models a scenario where each observation may be drawn from an unknown number of objects for which it provides a tractable Markov  ...  Our experiment results also demonstrate the advantages of our framework over the existing methods.  ...  Moreover, Bayesian nonparametric frameworks are introduced for tracking objects in high clutter [10] .  ... 
arXiv:2112.06640v1 fatcat:des52vsbezggpc6wlxrqbka67u

Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging

Mame Diarra Fall
2019 The International Journal of Biostatistics  
Nonparametric Bayesian methods provide a widely used framework that offers the key advantages of a fully model-based probabilistic framework, while being highly flexible and adaptable.  ...  The goal of this paper is to provide a motivation of Bayesian nonparametrics (BNP) through a particular biomedical application, namely Positron Emission Tomography (PET) imaging reconstruction.  ...  Nonparametric Bayesian methods provide a widely used framework that offers the key advantages of a fully modelbased probabilistic framework, while being highly flexible and adaptable.  ... 
doi:10.1515/ijb-2017-0099 pmid:31774734 fatcat:rollo6ubvzafnl7mjuu3oiidii

Object Based Unsupervised Classification of VHR Panchromatic and Multispectral Satellite Images by Combining the HDP, IBP and K-Mean on Multiple Scenes

Dipika R. Parate
2017 International Journal for Research in Applied Science and Engineering Technology  
In this paper Bayesian hierarchical model (HDP_IBPs) to classify very high resolution panchromatic as well as multispectral satellite images in an unsupervised way, in which the hierarchical Dirichlet  ...  clustering is a popular tool for exploratory data analysis, such as K-means clustering technique .Automatic determination of the initialization number of clusters in K-means clustering application is  ...  LITERATURE SURVEY Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images by Combining the HDP and IBP on Multiple Scenes [1] In this paper, author proposed, a nonparametric Bayesian  ... 
doi:10.22214/ijraset.2017.3022 fatcat:enhvbahfyjawnoblbwvitsglje

Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons

Asma Rabaoui, Nicolas Viandier, Emmanuel Duflos, Juliette Marais, Philippe Vanheeghe
2012 IEEE Transactions on Signal Processing  
To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced.  ...  Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a GNSS based localization context where the GPS information may be inaccurate  ...  RAO-BLACKWELLIZED PARTICLE FILTERING FOR DYNAMIC BAYESIAN MODELS A.  ... 
doi:10.1109/tsp.2011.2180901 fatcat:nbypnqtspvayjbowscyqzuq5di

Nonparametric Bayesian Modeling of Multimodal Time Series [chapter]

Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
2020 Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection  
In this chapter, we take a Bayesian nonparametric approach in defining a prior on the hidden Markov model that allows for flexibility in addressing the problem of modeling the complex dynamics during robot  ...  Zhou et al., Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, https://doi.  ...  To a certain extent, the non-parameterized Bayesian model is Research on robot multi-modal information sensing and fusion provides theoretical framework and application guidance.  ... 
doi:10.1007/978-981-15-6263-1_2 fatcat:ekhgg7l6bramjgqh3ioa4ywm3u

Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models

Genevieve Flaspohler, Nicholas Roy, Yogesh Girdhar
2017 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data.  ...  We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within  ...  This work defines a paradigm for including the ability of unsupervised neural models to discover useful, low-dimensional data representations within a Bayesian nonparametric topic modeling framework and  ... 
doi:10.1109/iros.2017.8202130 dblp:conf/iros/FlaspohlerRG17 fatcat:w7deizz6hvcexozreqpu4vy3fe

Bayesian Nonparametric Approaches to Abnormality Detection in Video Surveillance

Vu Nguyen, Dinh Phung, Duc-Son Pham, Svetha Venkatesh
2015 Annals of Data Science  
Therefore, in this paper we revisit the abnormality detection problem through the lens of Bayesian nonparametric (BNP) and develop a novel usage of BNP methods for this problem.  ...  In particular, we employ the Infinite Hidden Markov Model and Bayesian Nonparametric Factor Analysis for stream data segmentation and pattern discovery.  ...  We propose a framework for nonparametric data segmentation and multi-modal abnormality detection.  ... 
doi:10.1007/s40745-015-0030-3 fatcat:56wmpggdgraktipdvg5solnnp4

A Nonparametric Bayesian Multipitch Analyzer Based on Infinite Latent Harmonic Allocation

K. Yoshii, M. Goto
2012 IEEE Transactions on Audio, Speech, and Language Processing  
For efficient Bayesian inference, we used a modern technique called collapsed variational Bayes.  ...  It is based on hierarchical nonparametric Bayesian models that can deal with uncertainty of unknown random variables such as model complexities (e.g., the number of F0s and the number of harmonic partials  ...  Emiya (INRIA, France) for allowing them to use the valuable MAPS piano database [8] .  ... 
doi:10.1109/tasl.2011.2164530 fatcat:flb23455irfz5mds4jvxce3nei
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